Manufacturing

Small models, high quality: Inside BMW Group’s experiments evaluating domain-specific language models

Wed, 04 Mar 2026 17:00:00 +0000

A car you can talk to has been a longstanding dream, whether as the basis for television shows or more recent smartphone integrations.

One way of achieving better, more natural voice commands is by incorporating AI foundation models into vehicle systems, which offer more intelligence than traditional voice commands. AI foundation models can connect everyday questions with vehicle functions in a seamless dialogue. These models allow drivers to focus on the road ahead and enjoy every aspect of the journey while making interactions more intuitive.

While large language models (LLMs) offer powerful capabilities, they present one considerable drawback, at least in automotive settings: their reliance on consistent network access makes LLMs impractical for in-vehicle use due to potential lag and interruption.

To deliver reliable, next-level intelligence, BMW Group and Google Cloud successfully completed a proof of concept to build an efficient, reproducible solution to automate the workflows for fine-tuning, optimizing, evaluating, and deploying language models for specific domains, with special focus on small-language models, or SLMs. In this blog, we want to show results, findings and provide source code to encourage wider adoption.

“Finding the optimal trade-off for small-language models is a challenging, iterative process,” Dr. Céline Laurent-Winter, vice president, Connected Vehicle Platforms at BMW Group, said. “Automating the workflow for training, testing, and deploying domain-specific SLM allows a big push for our development efficiency. With automated pipelines, we can rapidly adapt models to our domain and rigorously test and evaluate them against domain-specific benchmarks. This allows us to iterate and optimize models in hours rather than days, in an automated, reproducible workflow.”

Small language models: small concept, big potential

Generative AI offers automakers powerful new capabilities, enabling complex voice commands. Before, it would have been almost impossible for a voice command system to understand a request like: “Find me a restaurant with vegetarian offerings along my route that is open now and has a customer rating higher than four stars.” With its language understanding and reasoning capabilities, gen AI can puzzle out such a request.

Integrating this intelligence, however, presents a challenge: Cloud-based LLMs are powerful but rely on a stable network to avoid frustrating lag. Conversely, onboard LLM are constrained by a vehicle’s limited computing hardware.

Small language models may offer an ideal balance — but finding the right trade-off between size and capability requires careful optimization.

These purpose-built, right-sized generative AI models can be run directly on edge devices, including vehicles. A common approach is having the SLMs handle the most frequently used features locally and only routing more complex requests to a cloud-based LLM. An SLM must be small enough to run on the target device, yet capable enough to be useful — especially when tailored to the specific automotive context via fine-tuning

Challenges of integrating foundation models into vehicles

Compared to the cloud, vehicle infotainment systems have limited storage and computing power. A 5 Series sedan or X3 SUV might look big, but there’s still limited space given all the performance, technology, and luxury that must fit between their four wheels.

Therefore, integrating a large language model, such as Gemma 3 27B which can consume over 40 GB of memory at 16-bit precision, is difficult. While smaller versions exist (e.g., Gemma 3 270M), they still tend to have a broad, generalized focus albeit with potential reduced accuracy compared to bigger models.

Hence, model compression (to reduce size) and tuning (to ensure high accuracy) become necessary for specialized use cases like ours. The goal then is finding the best tradeoffs between model size, inference time, and accuracy for the most frequent tasks.

Converting LLMs to SLMs

Turning large, resource-intensive LLMs into efficient SLMs requires well-known compression and quality enhancement techniques. Here’s a (reduced) overview of common techniques we’ve explored:

Compression techniques:

The primary goal is to reduce the model's compute and memory complexity. This can be done via:

Quantization: Reducing the model's memory footprint by converting high-precision parameters (e.g., 32-bit floats) to lower-precision formats (e.g., 8-bit integers or 4-bit floats). This leads, however, to a potential, but often minor, reduction in accuracy.
Pruning: Systematically identifying and removing the least important parameters or connections within the neural network, streamlining the SLM while retaining core capabilities.
Knowledge distillation: A compact "student" model is trained to replicate the performance of a larger "teacher" LLM, transferring complex knowledge into a much smaller, more efficient architecture.

Post-compression quality enhancement

We further engaged methods that can help recover or improve performance lost during compression.

Fine-tuning (and LoRA): Adapts the compressed model to a specific domain using targeted datasets. Standard approaches are Parameter-efficient fine-tuning (PEFT), such as Low-Rank Adaptation (LoRA). LoRA freezes the original weights and injects smaller, trainable matrices, dramatically reducing computational and storage costs while matching the performance of full fine-tuning.
Reinforcement Learning (RL): Methods like Proximal Policy Optimization (PPO), Direct Policy Optimization (DPO), and Group relative policy optimization (GRPO) are used for alignment with human preferences. RL iteratively improves model outputs by rewarding desired behaviors, guiding the model to generate more useful and accurate responses.

Evaluating performance for automotive tasks

Once a model has been compressed and enhanced, a crucial final step is to rigorously evaluate its performance. This covers system performance (e.g., latency, resource utilization on target hardware) and the qualitative assessment of the model's generated responses. For assessing quality, established methods are:

Point-wise evaluation: These methods assess the quality of a single generated response by comparing it against a pre-defined "ground truth" or reference answer. Examples include ROUGE and BLEU metrics.
Pair-wise evaluation: This approach determines which of two different model outputs is better, often aligning more closely with subjective human preferences for conversational quality. This can be executed with an Auto-rater (or LLM-as-a-judge) or direct Human Feedback.

Developing a robust testing strategy combining these evaluation methods is essential for validating the success of the compression and fine-tuning efforts.

The challenge of finding the optimal configuration

The path from a general-purpose LLM to a specialized SLM is not straightforward. Every choice — from type of quantization to characteristics and contents of the fine-tuning domain-specific dataset — directly affects the quality and efficiency of the final model. This creates an exponential range of possible configurations each with its own trade-offs.

This intricate landscape is further complicated by practical constraints: Not every compression or enhancement technique is applicable to every language model, and some methods are incompatible. For example, API-only models like Google Gemini permit fine-tuning only through a fixed set of methods.

The sheer volume of viable combinations renders a manual search for the optimal configuration an incredibly tedious, if not impossible, undertaking. To overcome this challenge, we built automated, reproducible workflows through executable pipelines.

Solution: An automated workflow for SLM optimization

Our solution is an automated workflow that orchestrates compression, adaptation, and evaluation steps needed to produce optimized SLMs. This is achieved by designing a flexible pipeline where each step is a modular, parameterized component. This workflow-based approach allows us to systematically explore the vast configuration space and pinpoint the best-performing models for in-vehicle deployment.

The process is structured as a workflow that can be executed automatically on a powerful workflow engine, such as Vertex AI Pipelines. In this workflow, we can define the sequence of operations (e.g., quantization, followed by LoRA fine-tuning and DPO) as a chain of interconnected components. Through pipeline parameters, we can search the entire configuration space, test different base models, compression techniques, tuning methods, and evaluation datasets.

This automated search allows for the comprehensive exploration of possibilities that would be unfeasible to test manually. The final artifacts from each pipeline execution are fully traceable and ready for deployment. This includes the versioned SLM itself, exact configuration parameters that produced the model, datasets used for evaluation, and a detailed report of its performance metrics, ensuring complete reproducibility.

Implementation: An automated workflow with Vertex AI Pipelines

Our solution is built on Google Cloud's Vertex AI platform, using configurable, executable pipeline templates. This offers a structured and automated way to find optimal SLMs in the vast possible search space. Figure 1 illustrates this workflow, its steps and their interactions with various data and model stores.

Figure 1: High-level overview of the automated pipeline's steps and its interaction with data and model stores.

Step 1: Versioning and configuration

Every Vertex AI workflow begins in Vertex AI Experiments. This initial step ensures the entire process is version controlled. The chosen LLM and datasets as well as the pipeline's configuration parameters are all logged as a single, versioned entity, ensuring complete traceability and reproducibility for every experiment.

Step 2: Optimization and compression

This stage puts the compression and enhancement techniques we discussed earlier into practice. Crucially, the pipeline is designed to manage the complex compatibility matrix between models, methods, and parameters. A pipeline template can, for example, enforce that only certain fine-tuning methods are applied to specific model architectures they are known to support, thereby automating the management of these constraints.

Our implementation provides reusable and standardized components for various fine-tuning (e.g., LoRA) and reinforcement learning methods (e.g., DPO, GRPO, and PPO). For compression, we adopt post-training quantization methods mapping models to lower-bit data types (e.g., bfloat16, 4-bit floats, or 8-bit integers) tailored to the target hardware's specifications.

Step 3: Conversion and deployment testing

Once an SLM is optimized, the pipeline deploys it to an environment. This allows testing if the model deployment succeeds on hardware representative of the target environment. This step provides a crucial, early validation point for the model's technical viability under realistic conditions.

An example would be running SLM on Android devices directly and natively (i.e. without emulation layers) on compute instances in the cloud. This allows testing how the model works on the target environment.

Step 4: Evaluation

A comprehensive evaluation is conducted to measure the SLM's true performance. This goes beyond simple accuracy, encompassing hardware-specific metrics like memory usage and inference latency as measured on the cloud-based device emulators. We also assess response quality using a combination of evaluation methods.

This can include point-wise metrics like ROUGE and BLEU, as well as more advanced pair-wise methods like auto-raters. The pipeline is designed to use custom test datasets reflecting a wide range of in-car tasks, such as multi-turn response generation or query rewriting with conversational context. This robust evaluation framework is also forward-looking, with the capability to assess multimodal SLMs such as Google Gemini and Gemma.

Step 5: Visualization and analysis

The Vertex AI Experiments allows storing generated metrics, comparing different experiment runs side-by-side, and creating visualizations using integrated tools like TensorBoard and Looker, making it easy to identify the most promising SLM candidates.

Figure 2: The automated pipeline as viewed in the Vertex AI Pipeline interface.

This entire automated workflow, from versioning to evaluation, creates a powerful feedback loop. It enables continuous integration and refinement, allowing teams to rapidly iterate and adapt their SLMs to evolving requirements and discover optimal configurations that would be almost impossible to find via manual efforts.

Conclusion and looking ahead

In this blog, we detailed how the automated workflow built on Google Cloud's Vertex AI successfully streamlines SLM development. This enables rigorous evaluation which model architectures or types (like Gemini, Gemma, and Llama) offer the best trade-off for our domain regarding performance, accuracy and size.

Crucially, we are linking our approach with the BMW Group’s "Head unit in the cloud", running the Android Open Source Project (AOSP) based infotainment system natively on cloud compute instances. This allows to test SLMs, including multimodal functions, in a virtual, scalable environment without the need for limited embedded devices.

The BMW Group's commitment to delivering cutting-edge in-vehicle experiences via artificial intelligence aligns seamlessly with Google Cloud's expertise in AI and machine learning. As we look ahead, we anticipate a continued partnership that will push the boundaries of what's possible in automotive AI.

We are publishing the solution of our PoC in the form of a SLM pipeline on GitHub. Feel free to adapt it to your needs and build your own optimized SLM!

_{This blog was written by Dr. Michael Menzel, Google Inc., and Dr. Jens Kohl, BMW Group, and is based on work done in a PoC which involved Dr. Arian Bär, David Katz, Dr. Felix Willnecker, Dr. Jens Kohl, Karsten Knebel, Dr. Manuel Luitz, Paul Weber, Raphael Perri, Thomas Riedl (all BMW Group) as well as Florian Haubner, Marcel Gotza, Dr. Michael Menzel, Raul Escalante (all Google Inc.).}

Auto-ISAC and Google partner to boost automotive sector cybersecurity

Mon, 05 Jan 2026 17:00:00 +0000

Ever since Carl Benz patented what is widely considered the first practical automobile and Henry Ford’s industrial techniques drove production scalability, consumer behavior and evolving preferences have driven improvements in performance, safety, and reliability. We are now at a phase of automotive development where continued automobile featurization — digital apps that provide the car with additional capabilities — heavily depends on cloud computing, high speed networks, and artificial intelligence.

As the car continues to evolve in the cloud era, and with 1.5 billion automobiles on the road today, we’re seeing a broad spectrum of bad actors increasingly targeting the automotive sector’s cloud environments, from factories to showrooms to consumer vehicles.

Today, Google Cloud is proud to join the Automotive Information Sharing and Analysis Center (Auto-ISAC) as an Innovator Partner, a move that significantly deepens our commitment to the automotive and transportation sectors. The Auto-ISAC is a global community that has come together to address vehicle cybersecurity risks. It serves as the industry's central nervous system for security, accounting for 99% of light-duty vehicles in North America, and uniting over 80 global OEMs and suppliers.

As the organization expands to include heavy trucking and the commercial vehicle sector, our partnership comes at a pivotal moment for the industry. Safeguarding the future of mobility demands a strategy that transcends traditional boundaries. Through this collaboration, Google Cloud has committed to dedicating resources and experts to work with industry leaders, fortifying the resilience of automotive systems against evolving threats.

We are bringing a network of expertise spanning IT, OT, supply chain logistics, and product security, specifically designed to navigate the complexities of the software-defined vehicle and industry 4.0. This partnership underscores our dedication to supporting the sector's digital transformation, while ensuring the integrity of its infrastructure.

By combining our global security intelligence with the Auto-ISAC’s collective defense model, we aim to provide the knowledge and support necessary for members to maintain vigilance, anticipate and mitigate threats, manage crises effectively, and ensure operational continuity in an increasingly complex cybersecurity landscape

As an Innovator Partner, Google Cloud will bring experts and resources, including unique insights from Mandiant, to help protect the automotive industry against cyberattacks. Googlers will work with automotive sector defenders and leaders , sharing knowledge that we have learned through building and deploying secure technology at Google scale.

This partnership is a continuation of Google’s commitment to invest at least $10 billion over five years to advance cybersecurity. This same commitment has enabled us to join other organizations like the Electricity ISAC, Health ISAC, Financial Services ISAC, BRC, MFG-ISAC and affiliates, so we can continue to support the security and resilience of our critical infrastructure across key sectors.

“As the automotive sector accelerates its shift toward software-defined vehicles, we recognize its vital role in driving the global economy. Our partnership with Auto-ISAC reflects a deep commitment to securing that transformation. By pairing Google’s global threat intelligence with the community’s industry expertise, we aim to foster a resilient ecosystem that protects the entire value chain — from the supply chain to the connected car,” said Nick Godfrey, senior director and global head, Office of the CISO, Google Cloud.

“This digital transformation is effectively integrating vehicles into the vast ecosystem of the Internet of Things, redefining every stage of the lifecycle from design and development to the customer experience. However, as the attack surface expands, security cannot be an afterthought. It is imperative that our investment in cyber resilience evolves in tandem with our innovation to truly safeguard the future of mobility,” said Vinod D’Souza, director, manufacturing and industries, Office of the CISO, Google Cloud.

“Google Cloud’s expertise in cybersecurity, scalable infrastructure, and artificial intelligence brings important capabilities to our membership. We are pleased to welcome Google Cloud to the Auto-ISAC’s Partnership Program and look forward to advancing vehicle cybersecurity together,” said Faye Francy, executive director of the Auto-ISAC.

Learn more

For more information about Google Cloud’s Auto-ISAC partnership, please visit the Google Cloud Office of the CISO.

Waze keeps traffic flowing with 1M+ real-time reads per second on Memorystore

Fri, 14 Nov 2025 17:00:00 +0000

Editor’s note: Waze (a division of Google parent company Alphabet) depends on vast volumes of dynamic, real-time user session data to power its core navigation features, but scaling that data to support concurrent users worldwide required a new approach. Their team built a centralized Session Server backed by Memorystore for Redis Cluster, a fully managed service with 99.99% availability that supports partial updates and easily scales to Waze’s use case of over 1 million MGET commands per second with ~1ms latency. This architecture is the foundation for Waze’s continued backend modernization.

Real-time data drives the Waze app experience. Our turn-by-turn guidance, accident rerouting, and driver alerts depend on up-to-the-millisecond accuracy. But keeping that experience seamless for millions of concurrent sessions requires robust and battle hardened infrastructure that is built to manage a massive stream of user session data. This includes active navigation routes, user location, and driver reports that can appear and evolve within seconds.

Behind the scenes, user sessions are large, complex objects that update frequently and contribute to an extremely high volume of read and write operations. Session data was once locked in a monolithic service, tightly coupled to a single backend instance. That made it hard to scale and blocked other microservices from accessing the real-time session state. To modernize, we needed a shared, low-latency solution that could handle these sessions in real time and at global scale. Memorystore for Redis Cluster made that possible.

aside_block: <ListValue: [StructValue([('title', 'Build smarter with Google Cloud databases!'), ('body', <wagtail.rich_text.RichText object at 0x7f6460ab7220>), ('btn_text', ''), ('href', ''), ('image', None)])]>

Choosing the right route

As we planned the move to a microservices-based backend, we evaluated our options, including Redis Enterprise Cloud, a self-managed Redis cluster, or continuing with our existing Memcached via Memorystore deployment. In the legacy setup, Memcached stored session data behind the monolithic Realtime (RT) server, but it lacked the replication, advanced data types, and partial update capabilities we wanted. We knew Redis had the right capabilities, but managing it ourselves or through a third-party provider would add operational overhead.

Memorystore for Redis Cluster offered the best of both worlds. It’s a fully managed service from Google Cloud with the performance, scalability, and resilience to meet Waze’s real-time demands. It delivers a 99.99% SLA and a clustered architecture for horizontal scaling. With the database decision made, we planned a careful migration from Memcached to Memorystore for Redis using a dual-write approach. For a period, both systems were updated in parallel until data parity was confirmed. Then we cut over to Redis with zero downtime.

Waze’s new data engine

From there, we built a centralized Session Server – our new command center for active user sessions – as a wrapper around Memorystore for Redis Cluster. This service became the single source of truth for all active user sessions, replacing the tight coupling between session data and the monolithic RT server. The Session Server exposes simple gRPC APIs, allowing any backend microservice to read from or write to the session state directly, including RT during the migration. This eliminated the need for client affinity, freed us from routing all session traffic through a single service, and made session data accessible across the platform.

We designed the system for resilience and scale from the ground up. Redis clustering and sharding remove single points of contention, letting us scale horizontally as demand grows. Built-in replication and automatic failover are designed to keep sessions online. While node replacements may briefly increase failure rates and latency for a short period, sessions are designed to stay online, allowing the navigation experience to quickly stabilize. And with support for direct gRPC calls from the mobile client to any backend service, we can use more flexible design patterns while shaving precious milliseconds off the real-time path.

Fewer pit stops, faster rides

Moving from Memcached’s 99.9% SLA to Memorystore for Redis Cluster’s 99.99% means higher availability and resiliency from the service. Load testing proved the new architecture can sustain full production traffic, comfortably handling bursts of up to 1 million MGET commands per second with a stable sub-millisecond service latency.

Because Memorystore for Redis supports partial updates, we can change individual fields within a session object rather than rewriting the entire record. That reduces network traffic, speeds up write performance, and makes the system more efficient overall – especially important when sessions can grow to many megabytes in size. These efficiencies translate directly into giving our engineering teams more time to focus on application-level performance and new feature development.

Session data in Memorystore for Redis Cluster is now integral to Waze’s core features, from evaluating configurations to triggering real-time updates for drivers. It supports today’s demands and is built to handle what’s ahead.

The road ahead

By proving Memorystore for Redis Cluster in one of Waze’s most critical paths, we’ve built the confidence to use it in other high-throughput caching scenarios across the platform. The centralized Session Server and clustered Redis architecture are now standard building blocks in our backend, which we can apply to new services without starting from scratch.

With that initial critical path complete, our next major focus is the migration of all remaining legacy session management from our RT server. This work will ultimately give every microservice independent access to update session data. Looking ahead, we're also focused on scaling Memorystore for Redis Cluster to meet future user growth and fine-tuning it for both cost and performance.

Learn more

Waze’s story showcases the power and flexibility of Memorystore for Redis Cluster, a fully managed service with 99.99% availability for high-scale, real-time workloads.

Learn more about the power of Memorystore and get started for free.
See Memorystore for Redis Cluster product documentation.

Driving for the Horizon: New Android Automotive solution on cloud offers faster builds

Mon, 08 Sep 2025 06:00:00 +0000

The automotive industry is in the midst of a profound transformation, accelerating towards an era of software-defined vehicles (SDVs). This shift, however, presents significant challenges for manufacturers and suppliers alike. Their priority is making great vehicles, not great software, though the latter now contributes — and is increasingly a necessity — to achieve the former. These OEMs must find ways to bring greater efficiency and quality to their software delivery and establish new collaboration models, among other hurdles to achieving their visions for SDVs.

To help meet this moment, we’ve created Horizon, a new open-source software factory for platform development with Android Automotive OS — and beyond. With Horizon, we aim to support the software transformation of the automotive industry and tackle its most pressing challenges by providing a standardized development toolchain so OEMs can generate value by focussing on building products and experiences.

In early deployments at a half-dozen automotive partners, we’ve already seen between 10x to 50x faster feedback for developers, leading to high-frequency releases and higher build quality. In this post we will outline how Horizon helps overcome the key impediments to automotive software transformation.

The Roadblocks to Innovation in Automotive Software Development

Today, traditional automotive manufacturers (OEMs) often approach software development from a hardware-centric perspective that lacks agility and oftentimes struggles to scale. This approach makes software lifecycle support burdensome and is often accompanied by inconsistent and unreliable tools, slowing down development.

OEMs face exploding development costs, quality issues and slow innovation, making it difficult to keep pace with new market entrants and the increasing demand for advanced features. Furthermore, most customers expect frequent, high-quality over-the-air (OTA) software updates similar to what they receive on other devices such as on their smartphones, forcing most OEMs to mirror the consumer electronics experience.

But a car is not a television or refrigerator or even a rolling computer, as many now describe them. Vehicles are made up of many separate, highly complex systems, which typically require the integration of numerous components from multiple suppliers who often provide "closed box" solutions. Even as vehicles have become more connected, and dependent on these connective systems for everything from basic to advanced operations, the vehicle platform has actually become harder, not easier, to integrate and innovate with.

We knew there had to be a better way to keep up with the pace necessary to provide a great customer experience.

Introducing HORIZON: A Collaborative Path Forward

To tackle these pressing industry challenges, Google and Accenture have initiated Horizon. It is an open-source reference development platform designed to transform the automotive industry into a software-driven innovation market.

Our vision for Horizon is enabling automakers and OEMs to greatly accelerate their time to market and increase the agility of their teams while significantly reducing development costs. Horizon provides a holistic platform for the future of automotive software, enabling OEMs to invest more in innovation rather than just integration.

Key Capabilities Driving Software Excellence

Horizon offers a comprehensive suite of capabilities, establishing a developer-centric, cloud-powered, and easy-to-adopt open industry standard for embedded software.

1. Software-First Development with AAOS

Horizon champions a virtual-first approach to product design, deeply integrating with Android Automotive OS (AAOS) to empower software-led development cycles. This involves the effective use of the vehicle hardware abstraction layer (VHAL), virtio, and high-fidelity cloud-based virtual devices like Cuttlefish, which can scale to thousands of instances on demand. This approach allows for scalable automated software regression tests, elastic direct developer testing strategies, and can be seen as the initial step towards creating a complete digital twin of the vehicle.

2. Streamlined Code-Build-Test Pipeline

Horizon aims to introduce a standard for the entire software development lifecycle:

Code: It supports flexible and configurable code management using Gerrit, with the option to use GerritForge managed service via our Google Cloud Marketplace for productive deployments. With Gemini Code Assist, integrated in Cloud Workstations, you can supercharge development by leveraging code completion, bug identification, and test generation, while also aiding in explaining Android APIs.
Build: The platform features a scaled build process that leverages intelligent cloud usage and dynamic scaling. Key to this is the caching for AAOS platform builds based on warmed-up environments and the integration of the optimized Android Build File System (ABFS), which can reduce build times by more than 95% and allow full builds from scratch in one to two minutes with up to 100% cache hits. Horizon supports a wide variety of build targets, including Android 14 and 15, Cuttlefish, AVD, Raspberry Pi devices, and the Google Pixel Tablet. Build environments are containerized, ensuring reproducibility.
Test: Horizon enables scalable testing in Google Cloud with Android’s Compatibility Test Suite (CTS), utilizing Cuttlefish for virtualized runtime environments. Remote access to multiple physical build farms is facilitated by MTK Connect, which allows secure, low-latency interaction with hardware via a web browser, eliminating the need for hardware to be shipped to developers.

3. Cloud-Powered Infrastructure

Built on Google Cloud, Horizon ensures scalability and reliability. Deployment is simplified through tools like Terraform, GitOps and Helm charts, offering a plug-and-play toolchain and allowing for tracking the deployment of tools and applications to Kubernetes.

Unlocking Value for Auto OEMs and the Broader Industry

The Horizon reference platform delivers significant benefits for Auto OEMs:

Reduced costs: Horizon offers a reduction in hardware-related development costs and an overall decrease in rising development expenses.
Faster time to market: By accelerating development and enabling faster innovation cycles, Horizon helps OEMs reduce their time to market and feature cycle time.
Increased quality and productivity: The platform enables stable quality and boosts team productivity by providing standardized toolsets and fostering more effective team collaboration.
Enhanced customer experience: By enabling faster, more frequent and higher-quality builds, OEMs can change the way they develop vehicle software, thus offering enhanced customer experiences and unlocking new revenue streams through software-driven services.
Strategic focus: Horizon underpins the belief that efficient software development platforms should not be a point of differentiation for OEMs; instead, their innovation should be focused on the product itself. This allows OEMs to devote more time and resources to software development with greater quality, efficiency, and flexibility.
Robust ecosystem: To ensure scalable, secure, and future-ready deployments across diverse vehicle platforms, Horizon aims to foster collaboration between Google, integration partners, and platform adopters. While advancing the reference platform capabilities, Horizon also allows for tailored integration and compatibility with vehicle hardware, legacy systems and compliance standards.

The Horizon ecosystem

It’s been said that the best software is the one you don’t notice, so seamless and flawless is its functioning. This is especially true when it comes to the software-defined vehicle, where the focus should be on the road and the joy of the trip.

This is why we believe the platforms enabling efficient software development shouldn’t be differentiating for automakers — their vehicles should be. Like a solid set of tires or a good sound system, software is now essential, but it’s not the product itself. That is the full package put together by the combination of design, engineering, development, and production.

Because software development is now such an integral part of that process, we believe it should be an enabler, not a hindrance, for automakers. To that end, the Google Cloud, Android, and Accenture teams have continuously aimed to simplify access and the use of relevant toolchain components. The integration of OpenBSW and the Android Build File System (ABFS) are just the latest waypoints in a journey that started with GerritForge as providing a managed Gerrit offering, and continuing with additional partners in upcoming releases.

Please, join us on this journey. We invite you to become a part of the community to receive early insights, provide feedback, and actively participate in shaping the future direction of Horizon. You can also explore our open-source releases on Github to evaluate and customize the Horizon platform by deploying it in your Google Cloud environment and running reference workloads.

Horizon is a new dawn for the future of automotive software, though we can only get there together, through open collaboration and cloud-powered innovation.

^{A special thanks to a village of Googlers and Accenture who delivered this,}^{Mike Annau, Ulrich Gersch, Steve Basra, Taylor Santiago, Haamed Gheibi, James Brook, Ta’id Holmes, Sebastian Kunze, Philip Chen, Alistair Delva, Sam Lin, Femi Akinde, Casey Flynn, Milan Wiezorek, Marcel Gotza, Ram Krishnamoorthy, Achim Ramesohl, Olive Power,}^{Christoph Horn, Liam Friel, Stefan Beer, Colm Murphy, Robert Colbert, Sarah Kern, Wojciech Kowalski, Wojciech Kobryn, Dave M. Smith, Konstantin Weber, Claudine Laukant, Lisa Unterhauser}

^—

^{Opening image created using Imagen 4 with the prompt: Generate a blog post header image for the following blog post, illustrating the concept of a software-defined vehicle <insert the first six paragraphs>.}

Build live voice-driven agentic applications with Vertex AI Gemini Live API

Mon, 05 May 2025 16:00:00 +0000

Across industries, enterprises need efficient and proactive solutions. Imagine frontline professionals using voice commands and visual input to diagnose issues, access vital information, and initiate processes in real-time. The Gemini 2.0 Flash Live API empowers developers to create next-generation, agentic industry applications.

This API extends these capabilities to complex industrial operations. Unlike solutions relying on single data types, it leverages multimodal data – audio, visual, and text – in a continuous livestream. This enables intelligent assistants that truly understand and respond to the diverse needs of industry professionals across sectors like manufacturing, healthcare, energy, and logistics.

In this post, we’ll walk you through a use case focused on industrial condition monitoring, specifically motor maintenance, powered by Gemini 2.0 Flash Live API. The Live API enables low-latency bidirectional voice and video interactions with Gemini. With this API we can provide end users with the experience of natural, human-like voice conversations, and with the ability to interrupt the model's responses using voice commands. The model can process text, audio, and video input, and it can provide text and audio output. Our use case highlights the API's advantages over conventional AI and its potential for strategic collaborations.

aside_block: <ListValue: [StructValue([('title', '$300 in free credit to try Google Cloud AI and ML'), ('body', <wagtail.rich_text.RichText object at 0x7f64607e0d90>), ('btn_text', 'Start building for free'), ('href', 'http://console.cloud.google.com/freetrial?redirectPath=/vertex-ai/'), ('image', None)])]>

Demonstrating multimodal intelligence: A condition monitoring use case

The demonstration features a live, bi-directional multimodal streaming backend driven by Gemini 2.0 Flash Live API, capable of real-time audio and visual processing, enabling advanced reasoning and life-like conversations. Utilizing the API's agentic and function calling capabilities alongside Google Cloud services allows for building powerful live multimodal systems with a clean, mobile-optimized user interface for factory floor operators. The demonstration uses a motor with a visible defect as a real-world anchor.

Here’s a summarized demo flow on a smartphone:

Real-time visual identification: Pointing the camera at a motor, Gemini identifies the model and instantly summarizes relevant information from its manual, providing quick access to crucial equipment details.
Real-time visual defect identification: With a voice command like "Inspect this motor for visual defects," Gemini analyzes the live video, identifies and localizes the defect, and explains its reasoning.
Streamlined repair initiation: Upon identifying defects, the system automatically prepares and sends an email with the highlighted defect image and part information, directly initiating the repair process.
Real-time audio defect identification: Analyzing pre-recorded audio of healthy and defective motors, Gemini accurately distinguishes the faulty one based on its sound profile and explains its analysis.
Multimodal QA on operations: Operators can ask complex questions about the motor while pointing the camera at specific components. Gemini intelligently combines visual context with information from the motor manual to provide accurate voice-based answers.

Under the hood: The technical architecture

The demonstration leverages the Gemini Multimodal Livestreaming API on Google Cloud Vertex AI. The API manages the core workflow and agentic function calling, while the regular Gemini API handles visual and audio feature extraction.

The workflow involves:

Agentic function calling: The API interprets user voice and visual input to determine the desired action.
Audio defect detection: Upon user intent, the system records motor sounds, stores them in GCS, and triggers a function that uses a prompt with examples of healthy and defective sounds, analyzed by the Gemini Flash 2.0 API to diagnose the motor's health.
Visual inspection: The API recognizes the intent to detect visual defects, captures images, and calls a function that uses zero-shot detection with a text prompt, leveraging the spatial understanding of the Gemini Flash 2.0 API to identify and highlight defects.
Multimodal QA: When users ask questions, the API identifies the intent for information retrieval, performs RAG on the motor manual, combines it with multimodal context, and uses the Gemini API to provide accurate answers.
Sending repair orders: Recognizing the intent to initiate a repair, the API extracts the part number and defect image, using a pre-defined template to automatically send a repair order via email.

Such a demo can be easily built with minimal custom integration, by referring to the guide here, and incorporating the features mentioned in the diagram above. The majority of the effort would be in adding custom function calls for various use cases.

Key capabilities and industrial benefits with cross-industry use cases

This demonstration highlights the Gemini Multimodal Livestreaming API's key capabilities and their transformative industrial benefits:

Real-time multimodal processing: The API's ability to simultaneously process live audio and visual streams provides immediate insights in dynamic environments, crucial for preventing downtime and ensuring operational continuity.

Use case: In healthcare, a remote medical assistant could use live video and audio to guide a field paramedic, receiving real-time vital signs and visual information to provide expert support during emergencies.

Advanced audio & visual reasoning: Gemini's sophisticated reasoning interprets complex visual scenes and subtle auditory cues for accurate diagnostics.

Use Case: In manufacturing, AI can analyze the sounds and visuals of machinery to predict failures before they occur, minimizing production disruptions.

Agentic function calling for automated workflows: The API's agentic nature enables intelligent assistants to proactively trigger actions, like generating reports or initiating processes, streamlining workflows.

Use case: In logistics, a voice command and visual confirmation of a damaged package could automatically trigger a claim process and notify relevant parties.

Seamless integration and scalability: Built on Vertex AI, the API integrates with other Google Cloud services, ensuring scalability and reliability for large-scale deployments.

Use case: In agriculture, drones equipped with cameras and microphones could stream live data to the API for real-time analysis of crop health and pest detection across vast farmlands.

Mobile-optimized user experience: The mobile-first design ensures accessibility for frontline workers, allowing interaction with the AI assistant at the point of need using familiar devices.

Use case: In retail, store associates could use voice and image recognition to quickly check inventory, locate products, or access product information for customers directly on the store floor.

Proactive maintenance and efficiency gains: By enabling real-time condition monitoring, industries can shift from reactive to predictive maintenance, reducing downtime, optimizing asset utilization, and improving overall efficiency across sectors.

Use case: In the energy sector, field technicians can use the API to diagnose issues with remote equipment like wind turbines through live audio and visual streams, reducing the need for costly and time-consuming site visits.

Get started

Explore the cutting edge of AI interaction with the Gemini Live API, as showcased by this solution. Developers can leverage its codebase – featuring low-latency voice, webcam/screen integration, interruptible streaming audio, and a modular tool system via Cloud Functions – as a robust starting point. Clone the project, adapt the components, and begin creating transformative, multimodal AI solutions that feel truly conversational and aware. The future of the intelligent industry is live, multimodal, and within reach for all sectors.

^{Special thanks to Michael Kollig for his leadership in this project.}

How AI will help address 5 urgent manufacturing challenges

Tue, 01 Apr 2025 16:00:00 +0000

In today’s dynamic business landscape, manufacturers are facing unprecedented pressure. The relentless pace of e-commerce combined with a constant threat of supply chain disruptions, creates a perfect storm. To overcome this complexity, leading manufacturers are leveraging the power of AI and integrated data solutions to not only survive, but thrive.

This week, at Hannover Messe, Google Cloud is announcing the latest release of its signature solution, Manufacturing Data Engine (MDE), to help manufacturers unlock the full potential of their operational data and drive AI transformation on-and-off the factory floor faster. We believe it will play a critical role in helping forward thinking leaders address five critical trends that are shaping the future of manufacturing.

1. B2B buyers demand digital-first experiences

Business buyers are increasingly adopting consumer-like behaviors, forgoing traditional, linear sales cycles. According to Gartner, 80% of B2B sales will be generated digitally in 2025. This shift demands a digital-first approach that extends beyond online storefronts to create seamless, personalized experiences across the entire customer journey.

For leading manufacturers, AI-powered user experiences can help address this shift in behavior. By leveraging AI to personalize product recommendations, streamline online ordering, and provide real-time customer support, manufacturers can meet the demands of digitally-savvy buyers.

2. Resilience is non-negotiable

The pandemic exposed the fragility of global supply chains and disruptions continue to be commonplace. According to Accenture, supply chain disruptions cause businesses to miss out on $1.6 trillion in revenue growth opportunities each year, on average. To increase resilience and address disruption isn't just a logistical challenge it requires a proactive approach. Manufacturers need to enhance visibility, improve forecasting, and leverage technology to identify and mitigate potential risks.

Multimodal AI can help improve supply chain management. By analyzing data from various sources like sensor data, visual inspections, and logistics tracking, AI can provide a holistic view of the supply chain, enabling proactive responses to disruptions.

aside_block: <ListValue: [StructValue([('title', 'Try Google Cloud for free'), ('body', <wagtail.rich_text.RichText object at 0x7f6470252d30>), ('btn_text', 'Get started for free'), ('href', 'https://console.cloud.google.com/freetrial?redirectPath=/welcome'), ('image', None)])]>

3. Bridging a digital skills gap

The manufacturing industry is facing a severe shortage of skilled workers, exacerbated by the rapid pace of technological advancements. Deloitte and The Manufacturing Institute found that there could be as many as 3.8 million net new employees needed in manufacturing between 2024 and 2033, and that around half of these jobs (1.9 million) could remain unfilled if the talent void is not solved. This talent gap poses a significant challenge to productivity, innovation, and long-term growth. Addressing the talent gap in manufacturing requires a multi-pronged approach. Manufacturers must invest in upskilling and reskilling their existing workforce, while also attracting and retaining top talent through competitive benefits and engaging work environments.

To empower existing workers and accelerate training, multimodal assistive search tools can provide instant access to relevant information through various formats like text, audio, and video. These tools enable users to verbally query for information, receive spoken answers or summaries of manuals, listen to step-by-step instructions, and even facilitate the creation of video-based training materials - rapidly enabling learning.

4. Sustainability is a business mandate (Enhanced by AI Agents)

Sustainability is now deeply intertwined with business success and 88% of manufacturers recognizing the critical role of technology in going green.. Consumers are increasingly demanding sustainable products and practices, and regulators are imposing stricter environmental standards. Manufacturers must embrace sustainable practices across their entire value chain, from sourcing raw materials to minimizing waste and reducing their carbon footprint.

To manage complex sustainability reporting, AI agents can automate data collection, and analysis.To help with compliance, agents can verify the materials and ingredients used against sources, track proper disclosures, and confirm adherence to mandated disclaimers.

5. Unlocking holistic insights

Many manufacturing organizations operate with siloed data residing in disparate departments and systems. The data is also incredibly diverse, often including Operational Technology (OT) data from the shop floor, Information Technology (IT) data from enterprise systems, and Engineering Technology (ET) data from design and simulation tools. This fragmentation, coupled with the differences in data formats, structures, and real-time requirements across these domains, can hinder manufacturers' ability to gain a holistic view of their operations. This leads to missed opportunities for optimization and inefficient decision-making.Breaking down these silos and establishing interoperability across OT, IT, and ET data is critical for unlocking the full potential of AI and driving truly informed business decisions.

As manufacturers integrate more data, the risk increases and AI-powered security becomes essential. AI can detect anomalies, facilitate threat intelligence including prevention, detection, monitoring and remediation - and ensure data integrity across interconnected systems, safeguarding sensitive information.

How does MDE and Cortex Framework help manufacturers address these 5 challenges?

Manufacturing Data Engine provides a unified data and AI layer that facilitates the analysis of multimodal data for better supply chain visibility, supports assistive search for bridging talent gaps, and enables AI agents to optimize sustainability initiatives. Furthermore, MDE helps contextualize various types of data, including OT, IT, and ET, allowing for richer insights and more effective AI applications. Critically, MDE aids in establishing a digital thread by connecting data back to its source, ensuring traceability and a holistic understanding of the product lifecycle. Moreover, Cortex Framework allows for the seamless integration of enterprise data with manufacturing data, enabling use cases like forecasting financial impact with machine data and optimizing production schedules based on demand signals.

See MDE in action at Hannover Messe and Google Cloud Next ‘25

We're excited to showcase this latest release at two major industry events:

Hannover Messe: Visit our booth to see live demonstrations of the new features and learn how MDE can help you drive industrial transformation.
Google Cloud Next: Join us at the Industry Showcase (Manufacturing) Booth to explore the latest advancements in our data and AI platforms, including Manufacturing Data Engine.

Unlock AI with IT and OT data powered by Manufacturing Data Engine with Cortex Framework

Tue, 01 Apr 2025 14:00:00 +0000

Breaking down the data silos between IT (business data) and OT (industrial data) is critical for manufacturers seeking to harness the power of AI for competitive advantage. This week, at Hannover Messe, Google Cloud is excited to announce the latest release of its signature solution, Manufacturing Data Engine, to help manufacturers unlock the full potential of their operational data and drive AI transformation on-and-off the factory floor faster.

In 2024, we delivered a number of enhancements to MDE to strengthen the integration between OT and IT data, and with initial technical foundation extensions for MDE to integrate with Cortex Framework. At the same time, the adoption of Cortex Framework, which helps customers accelerate business insights into their enterprise IT data, has grown beyond the traditional enterprise IT data sources from ERP, CRM, and ESG, to marketing and social media, and more.

Building on our progress, this latest MDE release completes our IT/OT integration journey and introduces powerful new features: Development Mode, historical metadata linking, Configuration Packages, to enable better data grounding of IT and OT data to drive faster AI outcomes. These advancements empower manufacturers to unlock deeper insights and achieve more with their data.

aside_block: <ListValue: [StructValue([('title', 'Try Google Cloud for free'), ('body', <wagtail.rich_text.RichText object at 0x7f643f999e20>), ('btn_text', 'Get started for free'), ('href', 'https://console.cloud.google.com/freetrial?redirectPath=/welcome'), ('image', None)])]>

Accelerating innovation with Development Mode: With Development Mode, manufacturers have more flexibility to delete configuration objects, which is particularly valuable in development and proof-of-concept (PoC) environments. This helps accelerate the innovation cycle by making it easier and less time-consuming to experiment with new data models.

Ingest time-shifted data with historical metadata linking: This feature uses event-time to map the correct metadata instances, which are extended with a "valid from" timestamp. This means manufacturers can load historical data at a later date and MDE will insert it into the right place in the timeline, ensuring accurate historical data representation of your data. This is helpful for manufacturers who need to load data out of order, and in turn makes it easier to analyze historical trends and patterns to optimize their operations.

Streamlining IT and OT with Configuration Packages: MDE Configuration Packages provide a powerful new way to merge factory floor data with your core enterprise systems by creating and packaging industry and use case-specific MDE configurations. Manufacturers can bridge the IT and OT gap, packaging their OT data from MDE in predictable schemas for integration within Cortex Framework alongside supply chain, marketing, finance, and sustainability data.

These powerful new features along with faster IT and OT data integration unlock a spectrum of transformative use-cases.

For example, manufacturers can visualize optimizing production schedules based on real-time demand signals from their marketing campaigns, or accurately forecast financial impacts by correlating machine performance with ERP financial data. They can enhance sustainability initiatives by analyzing energy consumption alongside production output.

Combine multimodal data from your factory with enterprise IT data for a holistic view of your operations

By contextualizing multimodal data from machines, sensors, and cameras with data from Cortex Framework, manufacturers gain a truly holistic view of their operations.

Unlocking new Gen AI use cases

Previously, manufacturers could combine OT data using MDE with Google AI services for things like faster issue resolution with ML-based anomaly detection, or flexible and scalable visual quality control.

With this release, we’re enabling even more possibilities for manufacturing intelligence by making it easier and faster to unify IT and OT data to use in grounding large language models (LLMs) for generative AI applications. Conversational Analytics lets you chat with your BigQuery data, Sheets, Looker Explores/Reports/Dashboards and more for generative analytics and insights. Imagine getting current open support cases from your customer support system, spotting an outlier, and being able to immediately ask for and trace the outlier part through to the production quality data from your factory floor to isolate the issue.

Use Conversational Analytics to get immediate, data-driven insights

By building on this latest release of MDE with Cortex Framework, in combination with Google Cloud’s AI capabilities, manufacturers can receive immediate, data-driven insights, empowering you to make smarter, faster decisions across your entire value chain.

Partner ecosystem: Driving customer success with Deloitte

We're proud to work with a robust ecosystem of partners who are instrumental in helping our customers achieve their digital transformation goals in manufacturing.

We're especially excited to announce that Deloitte has launched a packaged services offering for our latest MDE release, enabling customers to quickly leverage the new capabilities with services delivered by a trusted partner. Contact Deloitte to learn more, or visit their demo stand at the Google Cloud booth at Hannover Messe and at Google Cloud Next to understand how they can help you with your initiatives.

Looking ahead

Our latest release of MDE represents a significant milestone in our journey to empower manufacturers with the tools they need to thrive in the digital age. We're committed to continuous innovation and look forward to partnering with you on your industrial transformation journey.

Stay tuned for more updates and insights from Google Cloud.

See it in action at Hannover Messe and Google Cloud Next ‘25

We're excited to showcase this latest release at two major industry events:

Hannover Messe: Visit our booth to see live demonstrations of new features and learn how MDE can help you drive industrial transformation.
Google Cloud Next: Join us at the Industry Showcase (Manufacturing) Booth to explore the latest advancements in our data and AI platforms, including Manufacturing Data Engine, or join one of our Manufacturing-focused sessions.

Nuro drives autonomous innovation with AlloyDB for PostgreSQL

Mon, 24 Mar 2025 16:00:00 +0000

Editor’s note: Nuro, a robotics company that develops technology for self-driving vehicles, needed a data platform that could handle complex data processes and support continuous AI model improvement. By migrating to AlloyDB for PostgreSQL, Nuro gained the scalability, high performance, and advanced query capabilities needed to power AI-driven insights across millions of data points while reducing operating costs. AlloyDB AI further enables Nuro to perform complex similarity searches on vector embeddings, supporting continuous improvement.

Nuro’s mission is to make daily life better through robotics.

One of the ways we achieve this is with Nuro Driver, an AI-powered technology that automakers and mobility providers use to develop autonomous vehicles for personal use, delivery services, and ride-sharing applications.

Naturally, creating self-driving technology that’s truly safe and reliable takes more than just innovation — it requires a platform capable of processing vast amounts of data and adapting to continuous learning cycles. That’s why we needed data infrastructure that could handle our growing volumes of complex data and support essential processes like data discovery, labeling, and rapid evaluation.

As we navigated options for a managed SQL database that could handle these challenges and build on our existing PostgreSQL setup, we explored several options. We ultimately arrived at AlloyDB, a high-performance, fully managed PostgreSQL-compatible database on Google Cloud, for its superior performance, ease of use, and seamless integration.

Gearing up for autonomous data growth

Transitioning to a new data infrastructure can often be disruptive, but with AlloyDB, the process was seamless. The migration from our existing PostgreSQL environment required zero downtime and one-click setup. This allowed for continuous fleet operations without interruptions to deliveries or model training. AlloyDB now powers our core transactional and analytical workloads, managing crucial metadata for logs, trips, simulations, and real-time autonomy issues.

Operating across multiple cities, we rely on Google Cloud’s global availability to collect and manage petabytes of data for AI model training, evaluation, and simulation — with quick turn-around. This infrastructure enables analysis for refining route optimization to find challenging scenarios so our AI models can learn based on real-world on-road performance. AlloyDB plays a critical role in this ecosystem, efficiently processing large query volumes while supporting the rapid, data-driven decisions essential to autonomous operations.

Beyond performance, AlloyDB’s fully managed service reduced the burden of scaling and maintenance, allowing our team to focus on improving AI models rather than database administration. Its advanced query capabilities and deep integration with Google Cloud streamlined workflows, helping us iterate on autonomy models faster. With improved efficiency and reliability, our fleet can continuously evolve.

aside_block: <ListValue: [StructValue([('title', '$300 in free credit to try Google Cloud databases'), ('body', <wagtail.rich_text.RichText object at 0x7f6475dfc7f0>), ('btn_text', 'Start building for free'), ('href', 'http://console.cloud.google.com/freetrial?redirectPath=/products?#databases'), ('image', None)])]>

A data platform built for the long road ahead

Google Cloud is always innovating new ways to advance autonomous driving. We recently migrated all our vector embeddings to AlloyDB AI, enabling ML-based similarity searches across millions—and sometimes hundreds of millions—of vectors. With AlloyDB’s vector store and advanced indexing using ScaNN, our autonomy team can run complex similarity searches that quickly identify scenarios where Nuro Driver can learn and improve. AlloyDB’s high query performance for both transactional and analytical tasks ensures we can scale our dataset continuously, allowing us to train models on increasingly complex road conditions without performance bottlenecks.

To support these capabilities and improve performance, we’ve built a comprehensive ecosystem on Google Cloud. Cloud Storage serves as our primary storage for autonomy logs, on-road operation data, simulation records, and ML evaluation files. Using change data capture from Datastream, we replicate AlloyDB data to BigQuery in near real-time. This creates a unified flow that supports business dashboards and provides detailed, real-time analytics on autonomy performance. BigQuery serves as the main backend for analytical metrics, enabling precise evaluation and validation of the Nuro Driver.

Additionally, we use Spanner for storing log namespace metadata, while Firestore, Datastream, and Memorystore support various other applications, making our data management flexible and efficient. This diverse set of databases on a single cloud platform not only centralizes data management but also enables real-time insights and seamless data access. It’s the robust, scalable foundation we need to drive reliable autonomy at scale.

AlloyDB takes the driver’s seat in Nuro’s data transformation

Since migrating to AlloyDB AI, we've seen a substantial reduction in the operational costs of storing and searching embeddings. AlloyDB AI’s horizontal scalability has proven to be the most cost-effective solution for our needs, allowing us to add several new types of embeddings across applications without concerns over performance. With ScaNN indexing, our searches now yield over 20,000 high-precision results in seconds, outperforming alternative indexing methods like IVF and HNSW in both quality and scalability.

Our partnership with Google Cloud has also been invaluable. We have continuous access to innovations from the Google Cloud team, and we can easily meet any database requirement by leveraging their extensive suite of products. This support has accelerated our development, enabling us to focus on what matters most — advancing autonomous technology.

Looking forward, Google Cloud remains our primary cloud platform. Relying on its global presence and infrastructure, we can expand our services to new customers worldwide, all while maintaining the high standards of reliability and performance our team depends on. Google Cloud gives us the green light to tackle future challenges in autonomous driving, remove potential roadblocks, and keep innovation on the fast track.

Ready to get started with AlloyDB in your own environment? Check out the following resources:

Discover how AlloyDB combines the best of PostgreSQL with the power of Google Cloud in our latest e-book.
Try AlloyDB at no cost for 30 days with AlloyDB free trial clusters!
Learn more about AlloyDB for PostgreSQL.

Google Cloud first CSP to join BRC, MFG-ISAC, and affiliates to advance security

Thu, 12 Dec 2024 17:00:00 +0000

The AI phase of industrial evolution is marked by a profound transformation in how humans and intelligent machines collaborate. The blurring of boundaries between physical and digital systems across the manufacturing landscape is accelerating, driven by advancements in automation, robotics, artificial intelligence, and the Internet of Things.

This interconnectedness creates unprecedented opportunities for efficiency, innovation, and customized production. However, it also exposes manufacturers to a new generation of cyber threats targeting industrial operations, supply chains, and increasingly-sophisticated production processes. Safeguarding these critical assets requires a holistic approach that transcends traditional boundaries and embraces sector-wide collaboration.

To enhance our commitment to the manufacturing and industry sector, today we are announcing a new partnership with the Global Resilience Federation (GRF) by joining four of its affiliate groups: the Business Resilience Council (BRC), the Manufacturing Information Sharing and Analysis Center (MFG-ISAC), the Operational Technology Information Sharing and Analysis Center (OT-ISAC), and the Energy Analytic Security Exchange (EASE). Google Cloud is proud to be the first cloud service provider to partner with the GRF Business Resilience Council and its affiliates.

Through this partnership, Google Cloud will strengthen its commitment to the manufacturing industry by providing critical expertise and advanced security solutions. Our collaboration with industry leaders will focus on fortifying the resilience of manufacturing systems and supply chains against evolving cyber threats. This partnership underscores our dedication to supporting the manufacturing sector's digital transformation and modernization while ensuring the security and integrity of critical infrastructure.

In today's interconnected world, safeguarding your organization demands a comprehensive strategy that goes beyond traditional measures. Google Cloud will devote resources and experts to work alongside industry leaders to transform, secure, and defend the Manufacturing sector and will contribute to the manufacturing companies through a network of resources and expertise spanning IT, OT, industrial operations technology, supply chain, logistics, engineering technology, and product security, specifically designed to navigate the complexities of Industry 4.0 and 5.0.

This collaboration among professionals in cyber and physical security, geopolitical risk, business continuity, disaster recovery, and third-party risk management is critical for organizations with regional, national, and international footprints. In an era where the severity of cyber threats is constantly increasing, resilience is key. Partnerships fostered by GRF provide the knowledge and support necessary to maintain vigilance, manage crises, and navigate response scenarios to enable continuity of your operations.

As a GRF partner and a member of these four groups, Google Cloud will bring experts and resources — including unique insights from Mandiant, our Threat Horizon reports, and the Google Cloud Office of the CISO— to help the manufacturing and industry sector protect against cyberattacks. Google will work with defenders and sector leaders to share knowledge we’ve learned building and deploying secure technology.

This partnership is a continuation of our August 2021 commitment to invest at least $10 billion over five years to advance cybersecurity. This same commitment has enabled us to join other organizations including Health ISAC, Financial Services ISAC, and Electricity ISAC, so we can continue to support the security and resilience of our critical infrastructure across key sectors.

“Partnering with GRF and becoming a member of its affiliated groups BRC, MFG-ISAC, OT-ISAC, and EASE is a critical step in our commitment to help the manufacturing and industrial sectors transform and secure their critical infrastructure," said Phil Venables, VP and CISO, Google Cloud. "As a leading provider of cloud technologies and security solutions, we recognize the vital role these sectors play in driving economic growth and innovation. This partnership aligns with our dedication to supporting the modernization and resilience of manufacturing and industrial operations in the face of evolving cyber threats. By sharing our expertise and collaborating with industry leaders, we aim to raise awareness, develop innovative solutions, and strengthen the collective defense of these essential industries.”

“As a provider of innovative technology solutions, we recognize the vital role of the manufacturing and industrial sectors in driving our economy. This partnership reflects our commitment to supporting their transformation and strengthening their defenses against evolving cyber threats. Through collaboration and knowledge-sharing, we aim to foster a more secure and resilient future for these essential sectors,” said Nick Godfrey, senior director and global head, Office of the CISO, Google Cloud.

“Phil Venables and Google Cloud have long advocated for collaborative security and collective resilience, and their active role in the BRC and these communities brings invaluable expertise to help build a more secure ecosystem for businesses of all sizes — including their critical vendors and suppliers,” said Mark Orsi, CEO, GRF. “Google Cloud continues its leadership in advancing security and operational resilience across manufacturing, utilities, industrial, and critical infrastructure sectors — ultimately fostering a safer and more sustainable global supply chain.”

Google Cloud helps manufacturers bridge IT and OT data with Manufacturing Data Engine and Cortex Framework

Mon, 09 Sep 2024 13:00:00 +0000

Connecting operational technology (OT) and information technology (IT) has long been a goal for manufacturers to drive greater insights across their operations. Today, at the International Manufacturing Technology Show, Google Cloud is announcing an update to our Manufacturing Data Engine (MDE) to help bridge this divide and deliver greater productivity, innovation, and profitability to the industry.

We have now established initial technical foundation extensions for the Manufacturing Data Engine, Google Cloud’s signature manufacturing solution, to integrate with Cortex Framework, a packaged solution of reference architectures, deployment accelerators, and integrated services designed to speed up cloud deployments. Connecting IT data from Cortex Framework to OT data in MDE will provide manufacturers a holistic view of their factory and business operations that’s built on AI-enabled analytics and insights. This release will provide an initial set of features for MDE to support technical interoperability required for broader IT and OT use cases.

Historically, manufacturers have faced a disconnect between their physical machines (OT) and the data they generate (IT). This separation has led to siloed teams and inefficiencies. Bringing the two together provides the opportunity to apply analytical and AI tools to industrial data, which can help unlock new levels of automation and valuable insights for enterprises.

Connecting OT and IT data faster

Today, MDE is Google Cloud’s cornerstone solution for acquiring, processing and analyzing factory OT data. Cortex Framework helps customers accelerate business insights into their enterprise IT data, enabling better business outcomes, with less risk, complexity, and cost. With MDE joining the Cortex Framework solutions portfolio, manufacturers have access to a powerful combination of solutions to connect and harness the full potential of IT and OT information on a consolidated Google Cloud data and AI platform.

This powerful combination enables manufacturers to drive a more comprehensive view of their factory operations, uncover hidden insights, and drive intelligent decision-making by more easily collecting and processing multimodal data from machines, sensors, and cameras using MDE and then contextualizing it with data from core enterprise applications like SAP, Oracle, and Salesforce — as well as other external datasets via Cortex Framework.

Manufacturers can now achieve a holistic view of their entire operations, perform data visualization and analysis, and more effectively leverage AI on and off the factory floor.

This combined offering builds on successes like those achieved at companies like Tyson Foods, where MDE is already processing and contextualizing factory OT data. With Cortex Framework, manufacturers are enabled with access to additional IT data.

Accelerating operational excellence

With OT and IT data consolidated on Google Cloud, manufacturers also have the opportunity to accelerate operational excellence with smarter data- and AI-driven insights. By building on MDE and Cortex Framework — in combination with supporting services like BigQuery ML, Vertex AI, Gemini, and Timeseries Insights API — customers can tackle pressing industry challenges such as:

Linking the enterprise to factory floor insights: Contextualize shop floor data with enterprise data sources (e.g. production, supply chain, customer service, marketing) with MDE and Cortex Framework to identify new insights whether from marketing, sales, distribution, production, finance, or more.
Gaining end-to-end process insights: Connect sales orders to production orders, and then to overall equipment effectiveness and purchase orders, and get a holistic view of end-to-end processes.
Driving accurate and timely overall equipment effectiveness analytics: Monitor and optimize equipment and plant performance, availability, and quality at scale with actionable insights to drive production improvements and meet business requirements.
Operating more sustainably: Analyze telemetry data for utility consumption and waste to reduce costs and meet environment, social, and governance (ESG) goals. Combine transaction data from ERP with ESG data from partners like Dun & Bradstreet to elevate vendor performance management processes to new levels.

Innovating with AI on top

Faster root cause analysis with machine-level anomaly detection: Analyze telemetry data streams with self-training anomaly-detection machine-learning models to quickly understand where anomalous data was created by specific machines and/or processes providing a critical head start on root-cause analysis for faster corrective action.
Proactive and automated maintenance activity: Inform plant maintenance processes on transactional maintenance systems with AI-generated predictions for machine service needs via integrated telemetry and sensor data – helping to reduce downtime and maintenance costs.
Flexible and scalable visual quality control: Train and continuously improve vision AI models on Google Cloud, deploying them on the edge and ingesting the data back to the cloud for scalable and flexible analysis of quality assurance trends and easy access to details of specific defects and component quality.

By harnessing the power of data and AI, manufacturers can unlock new levels of agility, resilience, and competitiveness.

Come visit our Google Cloud at booth #236709 at IMTS to learn more.

Enabling modern manufacturing outcomes with AI, edge, and modern infrastructure

Wed, 22 May 2024 16:00:00 +0000

Emerging technologies, such as artificial intelligence (AI), edge computing, and software infrastructure are opening new doors for manufacturers to redefine operational efficiency, product quality, and safety standards. However, the complexity of implementing and scaling these cutting-edge solutions across diverse manufacturing environments and locations remains a challenge.

At the forefront of this technological wave is Google Distributed Cloud, a product that enables manufacturers to leverage the latest in AI, modern infrastructure, and security from Google Cloud directly on premises. Google Distributed Cloud can be deployed in a range of configurations, from self-managed software-only to fully managed hardware and cloud services, to meet OT security, latency, and availability requirements while bringing an agile platform to run modern applications onto the shop floor.

Manufacturing use cases

Google Distributed Cloud is transforming manufacturing operations in powerful ways across a variety of areas.

Visual inspection
Traditionally, quality control relied on human inspection, which was often time-consuming, error-prone, and costly. Edge-deployed AI models can perform visual inspection, analyze high-resolution images and video feeds in real time, and detect defects with unprecedented speed and accuracy. This real-time quality assurance translates into reduced waste, improved customer satisfaction, and the protection of brand reputation.

AI-driven visual inspection requires feeds from tens to hundreds of cameras to be analyzed at sub-second speed while continuously monitoring the performance of the AI models carrying out the analysis. As business needs drive changes to production lines, customers need the flexibility to update AI models to support new configurations. As a key component of the production process, visual inspection infrastructure has to operate reliably.

Automated process control
Similar to visual inspection use cases, automated process control generates vast amounts of data from Internet of Things (IoT) sensors or cameras embedded in production equipment. Modern process control infrastructure can leverage AI to perform micro-adjustments to machinery, optimize operations to yield higher quality with greater throughput, and reduce energy consumption and downtime.

Emerging workforce safety use cases, such as proactive hazard identification, leverage cameras and wearables, enabling real-time alerts or automated corrective measures to protect workers. Edge-based augmented reality (AR) enhances training and maintenance procedures, reducing human error and improving task efficiency. Preventing accidents and injuries, creates a safer work environment, resulting in reduction of physical harm to workers, machinery damage, and ultimately reducing costly disruptions.

Workforce safety and productivity
Integrating AI capabilities into existing manufacturing lines can bring new capabilities to legacy infrastructure and help avoid costly overhauls. Machine learning models running at the edge unlock valuable insights from existing equipment, enabling predictive maintenance to prevent malfunctions, extend equipment lifespans, and minimize costly downtime.

Modernization of legacy systems with AI
Google Distributed Cloud running on premise provides real-time responsiveness, simplified scalability, and operational resiliency to run the most demanding visual inspection workloads. In addition, Google Distributed Cloud also allows for efficient data filtering, aggregation, and analysis locally, reducing the need to send massive datasets to the cloud, thus optimizing bandwidth usage and reducing costs. Google Distributed Cloud brings the flexibility and seamless integration for modern OT and IT needs.

These are just a few examples of some of the exciting ways Google Distributed Cloud is helping to enhance manufacturing, but there are many other additional use cases and the list continues to grow.

What are the benefits of Google Distributed Cloud for manufacturing?

The advantages of Google Distributed Cloud for manufacturers extend far beyond technological improvements, translating directly into tangible business outcomes impacting cost, efficiency, safety, and resource management:

Reduced scrap: Enhancing quality control with AI can reduce defective products, resulting in less wasted raw materials and improved manufacturing efficiency.
Enhanced safety practices: The ability to identify hazards or potential accidents in real time helps minimize costly disruptions to operations and increases workers safety.
Accelerated insights: Cloud-native tools and processes enable rapid experimentation, iteration, and the development of new AI-powered solutions deployed at the edge, which can be tailored to specific needs. Google Distributed Cloud brings cloud and edge together in an efficient and integrated way, allowing manufacturers to shorten implementation cycles and drive competitive differentiation.
Improved sustainability: Enabling edge-driven process optimization, the identification of value add vs. non-value add tasks, waste reduction, and predictive maintenance can lead to long-term operational savings and environmental benefits.

The future of manufacturing

In the competitive world of manufacturing, the factories of tomorrow are being built today. By embracing edge computing with solutions like Google Distributed Cloud, manufacturers gain an essential tool to address the complex challenges of this dynamic industry. From increased automation and real-time insights to a commitment to safety and sustainability, Google Distributed Cloud paves the way towards intelligent, adaptive, and ultimately more successful manufacturing operations.

Learn more about how you can leverage Google Distributed Cloud on the manufacturing floor by downloading this report on delivering modern manufacturing insights. You can also come see how we are enabling innovation in person at our showcase at MxD (Manufacturing x Digital) where innovative manufacturers go to forge their futures and learn more about how you can leverage Google Distributed Cloud.

Automate plant maintenance using MDE with ABAP SDK for Google Cloud

Tue, 31 Oct 2023 16:00:00 +0000

Many Google Cloud customers run multiple manufacturing facilities and are constantly looking at ways to reduce costs and improve efficiency by proactively maintaining their machines and reducing production downtime.

Until now, monitoring and maintaining shop floor assets was mostly a manual, reactive, and cumbersome process. Google Cloud can help with its portfolio of artificial intelligence (AI) and machine learning (ML) capabilities. Today, sensors collect operational data from machines and stream It to Google Cloud. Once data is ingested in Google Cloud, customers can use machine learning or statistical calculations to identify patterns and predict and solve maintenance issues even before they happen. This solution pattern is known as predictive maintenance. Plant managers can now leverage technical solutions based on predictive maintenance to detect anomalies in any machine or equipment before it breaks down. Predictive maintenance also allows plant managers to schedule maintenance when it is convenient and not during actual production hours, thus saving a lot of time and cost.

Analyzing production data at scale for huge datasets is always a challenge, especially when there’s data from multiple production facilities involved with thousands of assets in production pipelines. To help solve this challenge, our Manufacturing Data Engine is designed to help manufacturers manage end-to-end shop floor business processes.

Manufacturing Data Engine

Manufacturing Data Engine (MDE) is a scalable solution that accelerates, simplifies, and enhances the ingestion, processing, contextualization, storage, and usage of manufacturing data for monitoring, analytical, and machine learning use cases. This suite of components can help manufacturers accelerate their transformation with Google Cloud’s analytics and AI capabilities.

The architecture above includes:

Manufacturing Connect, a factory connectivity product that supports edge workloads and integrates devices with Google Cloud. It has an extensive library of 250+ machine protocols that can quickly connect with nearly any manufacturing asset.
Manufacturing Data Engine, which processes, contextualizes, and stores factory data. With built-in data normalization and context-enrichment capabilities, it provides a common data model, with a factory-optimized data lakehouse for storage.
Customizable predefined analytics that provide customizable dashboards and ‘metamodels’ for business insights and self-service analytics.
AI-Driven Operations Optimization, a portfolio of AI use cases that unlock value at scale and integrate with Google Cloud tools such as Vertex AI.

SAP for manufacturing customers

SAP,a leading enterprise resource planning (ERP) software vendor, and Google Cloud have been partners for many years, helping customers transform their businesses leveraging Google’s services like BigQuery, Vertex AI, Cloud Pub/Sub, etc., across business domains. In the manufacturing space, SAP offers SAP Plant Maintenance to help industrial companies manage maintenance activities such as equipment inspection, notifications, corrective and preventive maintenance, and repairs.

ABAP SDK for Google Cloud

ABAP SDK for Google Cloud provides bi-directional, real-time integration between SAP and Google Cloud services. SAP developers can use the ABAP SDK for Google Cloud to integrate their SAP applications with Google Cloud services such as Vertex AI, Document AI, Translation AI, Pub/Sub, and more. With the ABAP SDK, customers can accelerate their digital transformation and achieve business goals faster.

Combining the best of both solutions

Manufacturing customers whose Plant Maintenance business processes are on SAP can implement predictive maintenance for their shop floor assets by combining capabilities of Google Cloud’s MDE and ABAP SDK for Google Cloud. Let’s take an example of a car manufacturer who uses sandblasting machines to clean metal parts before sending them out for a paint job and assembly. Imagine the car manufacturer wants to monitor the health of the motor in the blasting machine for any anomalies that may lead to assembly line disruptions:

MDE can receive a stream of sensor data in the form of shaft vibrations from the vibration sensors mounted on the motor, and MDE can have a machine learning model to detect any anomaly in the pattern of the vibration stream.
If an anomaly is detected by the ML model, an event is triggered in MDE and a notification with equipment information is published to a Cloud Pub/Sub topic.
An SAP automation program can run in the background at a regular interval and use ABAP SDK for Google Cloud to pull in the equipment information from Cloud Pub/Sub natively and create a Plant Maintenance Order in SAP.
An SAP business user starts working on the order. ABAP logic can be written to leverage the ABAP SDK for Google Cloud to publish the current order status to another Cloud Pub/Sub topic. This topic can then be used to source information to a BigQuery dataset.
A dashboard can be designed based on the BigQuery dataset for the plant managers which can be used to track metrics like:
- the status of the maintenance orders in real time
- visibility into delayed orders
- total delays for a month

Conclusion and value prospects

By integrating manufacturing data into enterprise software like SAP, a customer can gain unprecedented visibility into their shop floor operations and can:

Reduce downtime by planning and optimizing maintenance schedules, which can cost companies around 11% of their yearly turnover otherwise (Link)
Avoid catastrophic failures that otherwise could result in assembly line disruptions and expensive repairs
Achieve efficient cycle times in their production lines

MDE gives you the opportunity to streamline and gather your shop floor data efficiently, while ABAP SDK for Google Cloud opens up the possibilities to bring in manufacturing insights to your SAP systems. Customers can follow a similar solution pattern to utilize other Google Cloud solutions and services natively in their SAP systems and ABAP SDK for Google Cloud can enable the integration. Also, partners can convert these integration patterns into packaged solutions using the SDK.

Learn more and start your journey today with Google Cloud’s Manufacturing Data Engine and ABAP SDK for Google Cloud.

Five use cases for manufacturers to get started with generative AI

Mon, 09 Oct 2023 16:00:00 +0000

From the first assembly lines to the robotics revolution, the manufacturing industry continually strives to find new ways to boost productivity while lowering costs. Today, major trends are driving the need for further transformation, and generative AI is helping pave that path forward.

Factors like supply chain disruptions have wreaked havoc on bottom lines, with 45% of the average company’s yearly earnings expected to be lost over the next decade. Closer to home, companies are struggling to fill critical labor gaps, with over half (54%) of manufacturers facing worker shortages.

Challenges like these demand new solutions. And gen AI has the potential to deliver them. It can transform maintenance workflows and troubleshoot issues in real time. It can recommend ways to make production lines more efficient or less wasteful. It can even design new parts or products to take a manufacturing business to the next level.

By enhancing manufacturing processes, gen AI can reduce downtime, improve output, realize cost savings, and boost end-user satisfaction. No wonder 82% of organizations considering or currently using gen AI believe it will either significantly change or transform their industry (Google Cloud Gen AI Benchmarking Study, July 2023).

How to apply gen AI in manufacturing

With its unique ability to process and understand vast amounts of data, gen AI can be used across a wide array of applications — not just to improve productivity or efficiency. Here are five use cases that put gen AI to work in transforming the manufacturing industry.

1. Machine-generated events monitoring

Predictive maintenance is the best-practice strategy that identifies and rectifies possible equipment failures before they happen. According to Deloitte, it increases productivity by 25%, reduces breakdowns by 70%, and lowers maintenance costs by 25%.

Gen AI can play a key role in transforming maintenance workflows and staying one step ahead with predictive maintenance. It helps manufacturers optimize operations by interpreting telemetry from equipment and machines to reduce unplanned downtime, gain operating efficiencies, and maximize utilization. If a problem is identified, gen AI can also recommend potential solutions and a service plan to help maintenance teams rectify the issue. Manufacturing engineers can interact with this technology using natural language and common inquiries, making it accessible to the current workforce and attractive to new employees.

Watch this video to see how gen AI helps a transport company fix a problem with a faulty locomotive.

2. Customer service automation

The bar for after-sales service in manufacturing is getting higher. According to Salesforce, 80% of business buyers expect companies to respond and interact with them in real time, and 82% say personalized care influences their loyalty.

To deliver on these expectations, manufacturers are increasingly turning to gen AI — which provides a helpful, value-added customer service experience that automates and accelerates time-to-resolution for common interactions like product troubleshooting, ordering replacement parts, scheduling service, product information, and product operation.

Watch this video to see how gen AI improves customer service for an automotive manufacturer, delivering real-time support to the vehicle owner who sees an unexpected warning light.

3. Document search and synthesis

In manufacturing, product and service manuals can be notoriously complex — making it hard for service technicians to find the key piece of information they need to fix a broken part. Ordering and quoting can be very complex, too, with sales teams often having to decipher a huge array of information before creating a customer quote.

Gen AI can quickly sift through generations of documents throughout the product lifecycle, extracting and summarizing the information needed by sales teams and technicians. For example, it can present servicing instructions in an easily digestible, step-by-step format so technicians can get straight to work. And it can synthesize purchase orders and quickly provide customers a quote, eliminating the need for sales teams to manually cross-reference emails with inventory availability.

4. Product/content catalog discovery

Using gen AI, manufacturers gain an efficient method to match requirements to the specifications of products they buy, and provide the same service to their customers.

Gen AI-enabled sales applications can provide sales recommendations based on historical sales data, in-stock data, master data, and more. The sales recommendations can be generated using special machine learning algorithms equipped with continuous or real-time feedback functions to optimize the suggested results. Results could be combined with more descriptive statistics on sales data joined with meta-information that is uploaded by sales agents, giving a clear visibility into the buying process.

5. Supply chain advisor

As noted above, supply chain disruptions are having a significant impact on manufacturers. As well as dealing with these long-term disruptions, manufacturers are increasingly tasked with more responsible, ethical, and sustainable sourcing of materials. To enable this, visibility across the supply chain is the top priority for supply chain executives.

Gen AI can act as a supply chain advisor, providing greater visibility across complex networks and delivering recommendations for best-suited suppliers based on relevant criteria — such as bill of materials specifications, raw material availability and delivery schedules, or sustainability metrics. Adept at extracting provisions using natural language processing from legal and contractual documents, it can deliver real-time insights into supply chain performance to help improve decision-making.

Ready to get started?

Leading manufacturers are hitting the ground running with gen AI.

Global airline supplier, GA Telesis, has integrated Google Cloud’s gen AI technology to revolutionize sales processes. CEO Abdol Moabery said, “In aerospace, GA Telesis will deploy Google Cloud’s generative AI technology to revolutionize the sales and service processes for the parts the company supplies to major global passenger and cargo carriers.”

US Steel is building applications using Google Cloud’s generative artificial intelligence technology to drive efficiencies and improve employee experiences in the largest iron ore mine in North America.

“We’ve meaningfully accelerated digitization at U. S. Steel through our work with Google Cloud. Faster repair times, less down time, and more satisfying work for our techs are only some of the many benefits we expect with generative AI,” said David Burritt, president and CEO of U. S. Steel. “I’m thrilled that the U. S. Steel team is a manufacturing leader in this work.”

GE Appliances helps consumers create personalized recipes from the food in their kitchen with gen AI to enhance and personalize consumer experiences. GE Appliances’ SmartHQ consumer app will use Google Cloud’s gen AI platform, Vertex AI, to offer users the ability to generate custom recipes based on the food in their kitchen with its new feature called Flavorly™ AI. SmartHQ Assistant, a conversational AI interface, will also use Google Cloud’s gen AI to answer questions about the use and care of connected appliances in the home.

You can realize the transformative benefits of gen AI, too. Download our latest eBook, The executive’s guide to gen AI, for more details on jumpstarting your journey.

_{About the Google Cloud Generative AI Benchmarking Study}

_{The Google Cloud Customer Intelligence team conducted the Google Cloud Generative AI Benchmarking Study in mid-2023. Participants included IT decision makers, business decision makers, and CXOs from 1,000+ employee organizations considering or using AI. Participants did not know Google was the research sponsor and the identity of participants was not revealed to Google.}

How OPPO enhances AI capabilities on mobile devices with Google Vertex AI

Thu, 04 May 2023 16:00:00 +0000

Consumers today have more options than ever, which means businesses need to be dedicated to bringing the best-possible device performance to end users. At leading mobile device manufacturer OPPO, we’re constantly exploring ways to make better use of the latest technologies, including cloud and AI. One example is our AndesBrain strategy, which aims to make end devices smarter by integrating cloud tools with mobile hardware in the development process of AI models on mobile devices.

OPPO adopted this strategy because we believe in the potential of AI capabilities on mobile devices. On one hand, running AI models on end devices can better protect user privacy by keeping user data on mobile hardware, instead of sending them to the cloud. On the other hand, the computing capabilities of mobile chips are rapidly increasing to support more complex AI models. By linking cloud platforms with mobile chips for AI model training, we can leverage cloud computing resources to develop high-performance machine learning models that adapt to different mobile hardware.

In 2022, OPPO started implementing the AI engineering strategy on StarFire, which is our self-developed machine learning platform that merges cloud with end devices and serves, forming one of the six capabilities of AndesBrain. Through StarFire, we’re able to take advantage of various advanced cloud technologies to meet our development needs. To facilitate the AI model development process and enhance AI capabilities on mobile devices, we’ve collaborated with Google Cloud and Qualcomm Technologies to embed the Google Cloud Vertex AI Neural Architecture Search (Vertex AI NAS) on a smartphone for the first time. Let’s explore what we learned.

Challenges of developing AI models on mobile devices

One major bottleneck of developing AI models on mobile devices is the limited computing capabilities of mobile chips compared to computer chips. Before using Vertex AI NAS, OPPO’s engineers mainly used two methods to develop AI models that can be supported by mobile devices. One is simplifying the neural networks trained on cloud platforms through network pruning or model compressing to make them suitable for mobile chips. The other is adopting lighter neural network architectures built on technologies like depthwise separable convolutions.

These two methods come with three challenges:

Long development time: To see if an AI model can smoothly run on a mobile device, we need to repeatedly run tests and manually adjust the model according to the hardware characteristics. As each mobile device has different computing capabilities and memory, the customization of AI models requires significant labor costs and leads to long development time.
Lower accuracy: Due to their limited computing capabilities, mobile devices only support lighter AI models. However, after AI models trained on cloud platforms are pruned or compressed, the accuracy rate of the models decreases. We might be able to develop an AI model with a 95% accuracy rate in a cloud environment, but it won’t be able to run on end devices.
Performance compromisation: For each AI model on mobile devices, we need to reach a balance among accuracy rate, latency, and power consumption. High accuracy rate, low latency, and low power consumption can’t be achieved at the same time. As a result, performance compromises are inevitable.

Advantages of Vertex AI NAS for AI model development

The neural architecture search technology was first developed by the Google Brain team in 2017 to create AI trained to optimize the performance of neural networks according to developers’ needs. By automatically discovering and designing the best architecture for a neural network for a specific task, the neural architecture search technology enables developers to more easily achieve better AI model performance.

Vertex AI NAS is currently the only fully-managed neural architecture search service available on a public cloud platform. As OPPO’s machine learning platform StarFire is cloud-based, we can easily connect Vertex AI NAS with our platform to develop AI models. On top of that, we chose to adopt Vertex AI NAS for on-device AI model development because of the following three advantages:

Automated neural network design: As mentioned, developing AI models on mobile devices can be labor intensive and time consuming. Because the neural network design is automated through Vertex AI NAS, we can greatly reduce development time and easily adapt an AI model to different mobile chips.
Custom reward parameters: Vertex AI NAS supports custom reward parameters, which is rare among the NAS tools. This means that we can freely add the search constraints that we need our AI models to be optimized for. By leveraging this feature, we have added power as a search constraint and successfully lowered the energy consumption of our AI model on mobile devices by 27%.
No need to compress AI models for mobile devices: Based on the real-time rewards sent back from the connected mobile chips, Vertex AI NAS can directly design a neural network architecture suitable for mobile devices. The end result can be run on end devices without being further processed, which saves time and effort for AI model adaptation.

How OPPO uses Vertex AI NAS to enhance energy efficiency of AI models on mobile devices

Lowering power consumption is key to providing excellent user experience for AI models on mobile devices, particularly the computing intensive models related to multimedia and image processing. If an AI model consumes too much power, mobile devices can overheat and quickly drain their battery life. That is why the primary aim of using Vertex AI NAS for OPPO is to boost energy efficiency of AI processing on mobile devices.

To achieve this goal, we first added power as a custom search constraint to Vertex AI NAS, which only supports latency and memory rewards by default. This way, Vertex AI NAS can search neural networks based on the rewards of power, latency, and memory, letting us reduce power consumption of our AI models while reaching our desired levels of latency and memory consumption.

Then, we connected the StarFire platform with Vertex AI NAS through Cloud Storage. At the same time, StarFire is linked with a smartphone equipped with Qualcomm’s Snapdragon 8 Gen 2 chipset through the SDK provided by Qualcomm. Under this structure, Vertex AI NAS can constantly send the latest neural network architecture via Cloud Storage to StarFire, which then exports the model to the chipset for testing. The test results are sent back to Vertex AI NAS again through StarFire and Cloud Storage, allowing it to conduct the next round of architecture search based on the rewards.

This process was repeated until we achieved our target. In the end, we realized a 27% reduction in power of our AI model and a 40% reduction in computing latency, while maintaining the same level of accuracy rate before the optimization.

Broadening the application range

The first successful AI model optimization through Vertex AI NAS is truly exciting for us. We plan to deploy this energy efficient AI model on our future smartphones, and implement the same model training process supported by Vertex AI NAS in the algorithm development of our other AI products. Besides power, we also hope to add other reward parameters, such as bandwidth and operator friendliness, as search constraints to Vertex AI NAS for more comprehensive model optimization.

Vertex AI NAS has significantly facilitated the optimization of our AI capabilities on smartphones, and we believe that there is still great potential to explore. We will continue collaborating with Google Cloud to expand our use of Vertex AI NAS. For the developers who are interested in adopting Vertex AI NAS, we advise targeting the most relevant hardware reward parameters before launching the development process, and becoming familiar with the ways to build search spaces if custom search constraints are needed.

^{Special thanks to Yuwei Liu, Senior Hardware Engineer at OPPO, for contributing to this post.}

Timeseries Insights API for low latency anomaly detection at scale is now GA

Thu, 13 Apr 2023 10:00:00 +0000

Google Cloud is excited to announce the general availability of Timeseries Insights API, a powerful and efficient service for large-scale time-series anomaly detection in near real-time. Designed to help businesses gain insights and analyze data from various sources such as sensor readings, clicks, and news, the Timeseries Insights API allows businesses to process terabyte (TB) sized time-series data with sub-second response times.

Timeseries Insights API works natively with Google Cloud’s Manufacturing Data Engine. Manufacturers can easily tap into this powerful service to drive anomaly detection at scale on data stored by this solution.

Key features and benefits

Google Cloud’s Timeseries Insights API offers several significant benefits, including:

Anomaly and trend detection: The API enables users to detect trends and anomalies across multiple event dimensions.
Large scale: It can handle TB-scale datasets consisting of tens of billions of events and running thousands of queries per second.
Low-latency queries: The API is capable of returning query results in near real-time, with sub-second latencies, making it suitable for use as a backend for interactive, user-facing applications.
Real-time analysis: With its streaming update interface, the API allows users to perform real-time, context-dependent analysis over time-series data.
Serverless and fully managed: The Timeseries Insights API is fully managed, allowing users to focus on insights rather than managing infrastructure.
Flexible query language: The service comes with an intuitive API and simple parameters, making it easy to construct queries.

Use cases and early adopters

Several industries can benefit from time-series data prediction and analysis offered by the Timeseries Insights API. IoT companies can correlate multiple sensor data sources in real-time, content providers can monitor news and event streams to identify trending clusters, and network infrastructure and monitoring providers can analyze traffic logs for anomalous behaviors.

The GDELT Project (Global Database of Events, Language, and Tone) is a massive open data project that aims to monitor the world's news media in real-time to provide a comprehensive and open repository of global human society's behaviors, beliefs, and attitudes. GDELT today encompasses more than 8.5 trillion data points spanning global events and narratives in 150 languages across text, television, radio, and image-based news dating back 200 years, and updating in real-time from almost every country on earth.

The Timeseries Insights API allows GDELT to sift through this immense planetary-scale archive to identify the earliest glimmers of tomorrow’s biggest stories in real time.

“GDELT uses Google Cloud’s AI APIs, including the video, vision, speech-to-text, natural language and translation APIs to annotate a massive global firehose of data,” says Kalev Leetaru, the founder of the GDELT Project. “The Timeseries API allows us to look across all those annotations to see everything from the vertical surges of breaking events to the gradual subtle ebbs and flows of slowly unfolding stories to every anomaly in between — all in real-time.”

Integration with Google Cloud’s Manufacturing Data Engine

Anomaly detection can help manufacturers identify and address potential problems before they cause disruption, damage, or downtime. By monitoring data from sensors and other sources, customers can identify unusual patterns that may indicate equipment failure, quality issues, or other problems. With integrated alerting, maintenance engineers and factory supervisors can take preventive action early on.

Most importantly, anomaly detection based on Timeseries Insights API can be implemented fast, at scale, and at low cost. As a fully managed service that automatically learns from unlabeled data, it can be quickly rolled out to any machine, with no machine learning (ML) expertise or domain-specific input required. This is in stark contrast to predictive maintenance, which requires more costly machine-by-machine setups and is therefore often limited to individual, highly critical machines.

Anomaly detection built on Timeseries Insights API also differs meaningfully from traditional threshold-based services. Besides requiring little to no human input or data labeling, it can detect any events of significance, including those that may be within the normal operating range of a threshold-based system.

As the Timeseries Insights API becomes generally available, businesses now have access to a powerful tool that can help them unlock insights from their time-series data, enabling them to optimize operations, reduce downtime, and stay ahead of the competition. To learn more and get started, check out the Timeseries Insights API documentation.

Lighting the way: How ASML revived Moore's Law and remade chipmaking

Tue, 28 Mar 2023 16:00:00 +0000

Declaring the death of Moore’s Law has become something of a popular pastime in tech circles, based on the assumption that chips will have to stop doubling in power every two years because there has to be a limit to how many transistors can fit on them. Right?

And yet, chipmakers continue to defy expectations — and to some degree, physics. Beyond the boundaries of what previously thought possible, computer chips keep getting smaller and more powerful with the help of new technologies, many of which live on the cloud.

Artificial intelligence has helped researchers design better chips. High-performance computing lets scientists model those designs faster than ever. Big data and analytics allow for the better organization of information and faster test cycles. The end of Moore’s Law may well be on the horizon, but that hasn’t stopped the semiconductor industry from using every cutting-edge technology at its disposal to push that seeming inevitability further down the road.

One major reason why Moore’s Law remains alive and well has been the work of Dutch firm ASML.

The company builds photolithography machines, software, and solutions that allow manufacturers to make ever smaller, more efficient, and powerful computer chips. After 20 arduous years of research and development, ASML had a major breakthrough in 2017 on the machines that enable high-end chip manufacturing.

The result: a photolithography machine that uses wavelengths of extreme ultraviolet (EUV) radiation to make features in microchips as small as 13 nanometers. MIT Technology Review called it “the machine that saved Moore’s Law.”

The EUV machine took two decades of R&D, with industry experts openly doubting whether it would ever come to fruition. But through sheer determination, billions of dollars of investment, and some clever cloud-based tools, ASML finally broke through and is currently the only vendor in the world that offers the solution. The company has since been selling its machines to the top chipmakers, including Samsung, Intel, and TSMC — at a crucial time, no less, given the chip shortages and related geopolitical tensions.

Arnaud Hubaux, product cluster manager at ASML, borrowed a story from one of those customers on the latest episode of the Transformation Debrief video series to highlight just how focused the mission for the EUV machines has been: “I think Mark Liu, the chairman of TSMC, phrased it beautifully. He said, ‘The semiconductor industry for the past 50 years has been working in a tunnel.’ Everyone knew exactly where to go. The end was clear, the goal was clear: to shrink transistors and make them smaller. So everything with ASML was focused on that.”

The EUV machine, along with all of ASML’s other products and services, need to be extraordinarily precise. Semiconductor manufacturing requires a very complicated set of equipment that possess speed, efficiency, and accuracy to the smallest degrees. As much as the company has produced stunning innovation to build its EUV solution, it has also broken new ground with testing and measurement.

To advance many of these breakthroughs, ASML has utilized cloud technology and artificial intelligence to make its processes more efficient, speed up its testing and quality assurance cycles, and identify potential errors before they become serious problems. Even with the less cutting edge machines, like the “deep ultraviolet,” or DUV, machines that preceded EUV, the horsepower of the cloud is helping drive these enhancements and improvements, too.

“We had to make better sensors, better actuators, better light sources,” Hubaux said. “What you see now happening is that those machines are good enough, though. And, especially with another chip shortage, you want to optimize the utilization of those machines.”

That’s where the programs building and operating the machines are becoming as important as the machines themselves. “The optimization of those machines is something that you typically drive with software,” Hubaux said, “and that requires massive data processing.”

Massive data on the smallest scales

Hubaux has previously described ASML’s photolithography machines as “engineering a race car for an F1 driver.” Considering the miniscule scale at which chipmaking takes place, there is little to no margin for error. Everything in a photolithography machine needs to be calibrated down to the nanometer. With such a complicated machine, that kind of analysis and calibration creates a lot of data.

“We realized that our local environment, the environment we had on-prem, was not sufficient to drive the analysis we wanted to do, the product we wanted to build,” Hubaux said of ASML’s decision to migrate to Google Cloud several years ago.

ASML worked with Google Cloud partner Rackspace and machine learning specialist ML6 to build the architecture and artificial intelligence models needed to ingest, process, and parse in near real-time.

ASML's EUV machines work at the extreme ends of light and physic in a way that is still scalable and cost effective. Cloud-based high-performance computing was essential to R&D for the new machines.

“We noticed quickly after six months that the performance of the team improved 10 times,” Hubaux said. “The overall cycle of application development improves significantly.”

The primary motivation to migrate to the cloud and institute machine learning models that could help analyze its massive amounts of data was so AMSL could speed up the time with which it brought products to market. In the cloud, ASML engineers could run more tests and understand the results more quickly, accelerating R&D and production.

“The main driver was time to market,” Hubaux said. “We needed to be faster at releasing our solutions and also improving the quality of our solutions.”

On that front, the team more than succeeded, achieving a 40% improvement on time-to-market speeds.

Creating a competitive edge with cloud and AI

Hubaux notes that the cloud has yet to be widely adopted in the chipmaking industry. It’s a sector where intellectual property is guarded like a dragon’s lair full of gold and the manufacturing processes of extremely complex machines do not lend themselves to easy data gathering from sensors with an internet connection. All that being said, ASML has found that it can apply the cloud and machine learning to a variety of other properties within its operation to increase speed and efficiency.

“I expect that in the coming two to three years, there will be as slow a shift towards the cloud for the non-critical processes,” Hubaux said. “Those non-critical processes include diagnostics and monitoring that tend to be very data intensive.

ASML’s engineers welcomed the cloud, which allowed them to offload tasks that previously had to be performed manually.

“The engineers who had to deliver the solutions were super excited to onboard to the cloud, because for them, they could use state-of-the-art technology,” Hubaux said. “Everything they had had to maintain themselves — like updating tools, maintaining connections, securing the environment — was something that was very labor intensive, and all of a sudden all those hurdles were removed, so they could really focus on delivering what they were supposed to deliver, which is model good software.”

The chip manufacturing industry is one in which everything that happens needs to be explainable, down to the tiniest detail. ASML’s clients need to understand everything about how the machine works — its sensors, diagnostics, monitoring, and maintenance — to keep pumping out chips. As such, neural network-based artificial intelligence can be a difficult sell because of the “black-box” nature of the algorithms.

In other words, chipmakers could have been hesitant to use ASML’s machines unless they know exactly the outcomes any AI might deliver.

“When you are talking about high-volume manufacturing,” Hubaux said, “those machines have been tuned so much that any change to those machines could lead to a potential half-life down the line. That could really stop production, and cost customers millions. For that kind of scenario, machine learning is a thing not very widely adopted yet because of this black-box effect.”

Quality assurance, and assuring continued chip innovation

One area where ASML has seen success with AI is in quality assurance. Chips are very small and thus nearly impossible to manually inspect. But the team was able to train AI to quickly look for defects with low-resolution images of chips by training inspection models.

“This is where AI kicked in because we still used a physics model to classify a picture as good, bad, a defect, or a non-defect based on physics,” Hubaux said. “We used AI that we trained on high-resolution images to recognize low resolution images, filling in the blanks with the physics model.”

“So that's where AI is actually enhancing the capability of the hardware, to make this kind of virtual extension of the hardware that is improving the quality for predictions,” he continued. “And it’s also the big benefit for the customer, making it cheaper.”

Ultimately, ASML has threaded an extraordinary needle.

It has built one of the most advanced machines in history with its EUV photolithography equipment, driving the next generation of chips. It has also been able to take those advances and increase the company’s efficiency, optimizing its processes through the cloud and artificial intelligence in an industry where those solutions are often difficult to implement.

In a way, it’s a story as old as Moore’s Law, if not much older. Technology drives advancing technology, which advances new technology, which advances new technology . . . ever onward. And ASML is at the heart of these advances, driving its business to advance others, and setting itself up for the next leap forward . . . ever onward.

How B2B digital commerce will unfold in 2023

Thu, 23 Mar 2023 16:00:00 +0000

Editor’s note: Google Cloud partner commercetools shares how modern technologies like composable commerce, cloud-native infrastructure and artificial intelligence/machine learning (AI/ML) will lead the way in business-to-business (B2B) digital commerce this year.

Digital commerce in B2B has been predicted as the next big thing for years; yet, at the start of COVID-19, 60% of B2B companies had zero or limited eCommerce capabilities. The pandemic accelerated digitization and eCommerce has finally taken off: As of February 2022, 65% of B2B companies offered eCommerce capabilities.

The behavior of B2B buyers is also changing: Consumer-like expectations are at the heart of successful B2B commerce, and this is how manufacturers, distributors and wholesalers will shape their customer experiences. Today, 73% of B2B buyers want a personalized business-to-consumer or B2C-like experience. 83% prefer ordering or paying through digital commerce and 72% are eager to purchase across channels.

With digital commerce dictating how B2Bs will grow in 2023 and beyond, what trends will spur digital transformations across this business model? Here’s what the team at commercetools expects to unfold in B2B eCommerce this year.

#1 B2B firms are switching to cloud-native, composable commerce

B2B players still plagued with manual processes and siloed backend systems will move away from monolithic platforms and choose composable commerce. In a nutshell, composability enables businesses to select best-of-breed components, such as search, cart or checkout, and “compose” them into a custom application.

B2B firms will modernize their commerce backend, interoperating siloed systems like Configure Price Quote solutions (CPQs) for sales and enterprise resource planning solutions (ERPs) for order entry with an API-first and composable commerce stack. They will also pivot from on-premise deployments to cloud-native architectures as the baseline for auto-scaling capabilities instead of pre-provisioning online capacity during traffic peaks. That way, B2Bs can customize customer-centric experiences to boost revenue while reducing the complexity and cost of in-house IT infrastructure, as well as gaining operational efficiencies.

B2Bs will maximize the cross-section of composable commerce and cloud-native infrastructure by leveraging a commerce backend like commercetools Composable Commerce hosted on Google Cloud. This combined solution provides commercetools’ ready-to-use components built as microservices and exposed as APIs, such as product information management (PIM) and unified cart, integrated through the Google Cloud Marketplace.

#2 Strong focus on data quality and personalization

Focusing on data quality continues to be a big trend in 2023. B2B buyers expect product, pricing, inventory and shipping data points to be accurate across every touchpoint so they can make better purchasing decisions, such as when to order products and calculate quantities.

With so many data points to capture throughout the customer journey — product, inventory, pricing and customer data — we’ll see more B2B companies reorganizing their vast information pools to elevate customer experiences. They will pivot to modular and API-first solutions, plus flexible data models, so they can break data silos from legacy monolithic platforms and access such data when needed.

We also expect to see more customer analytics to unlock data on buyer behavior. By understanding what customers see, click and add to their shopping lists, B2B businesses get valuable insights into how buyers behave, using this data in the shopping journey according to product interests. That way, it’s possible to offer personalized experiences across touchpoints without hassle.

“It is important for B2B companies to look at their data as if it is one of their products; invest in its upkeep and integrity while finding ways to continuously improve it. Using advanced analytics powered by AI and ML to identify patterns from large amounts of data, B2B companies can activate insights into customer decision journeys to maintain loyalty, personalize experiences to improve satisfaction and boost revenue, while also finding ways to optimize costs. For example, with analytics, enterprises can streamline spend to focus on the highest-performing channels and reduce waste.” — Carrie Tharp, Google Cloud VP of Retail and Consumer

With data-driven tools coming into play like Google Cloud’s Discovery AI, Recommendations AI and Vision Product Search connected with composable commerce, B2B players can boost customer analytics to personalize experiences, improve customer satisfaction and reduce churn.

#3 The B2B customer experience will be redesigned

B2B players are taking a page out of the B2C playbook to elevate experiences throughout the customer journey. While intense work needs to happen in the backend commerce engine, B2B players will also redesign their digital frontends. That means boosting website performance, while mobile responsiveness and personalization will be at the forefront of these advanced digital initiatives.

More than ever, B2B companies are looking for digital storefronts delivered as progressive web applications (PWAs) for optimized performance and responsiveness across devices, as well as fast-loading and responsive experiences to boost your digital presence, SEO rankings and conversion rate. B2Bs can further streamline frontend development with solutions natively connecting to Google Cloud Marketplace, which supports a variety of storefront providers, including commercetools Frontend.

Leveraging Google Cloud’s unique capabilities, such as PWA web app development, Google Cloud Discovery AI solutions that include Retail Search and Vision API Product Search, among many others, B2B companies are well positioned to boost digital commerce in the years to come.

What’s next in 2023?

2022 was already a turbulent year; for better or worse, 2023 is expected to have a similar fate. For B2Bs, even the ones with tight budgets, investing in digital commerce can help future-proof businesses for whatever’s happening this year. To dive deeper into all predictions and insights by commercetools in collaboration with Google Cloud, read the guide Pivotal Trends and Predictions in B2B Digital Commerce in 2023.

Built with BigQuery: How BigQuery helps Leverege deliver business-critical enterprise IoT solutions at scale

Tue, 14 Feb 2023 17:00:00 +0000

Introduction

Leverege is a software company that enables market leaders around the globe to quickly and cost effectively build enterprise IoT applications to provide data-centric decision capability, optimize operations, improve customer experience, deliver customer value, and increase revenue. Leverege’s premier SaaS product, the Leverege IoT Stack, runs natively on Google Cloud and seamlessly integrates with Google’s vast array of AI/ML products.

Leverege uses BigQuery as a key component of its data and analytics pipeline to deliver innovative IoT solutions at scale. BigQuery provides an ideal foundation for IoT systems with its data warehousing capabilities, out-of-the-box data management features, real-time analytics, cross cloud data integration, and security and compliance standards. These features enable customers to easily integrate data processes and use the resulting datasets to identify trends and apply insights into operations.

Context and IoT industry background

The Internet of Things (IoT) connects sensors, machines, and devices to the internet, allowing businesses in every industry to move data from the physical world to the digital world, on the edge and in the cloud. The adoption of large-scale IoT solutions gives businesses the data they need to improve efficiency, reduce costs, increase revenue, and drive innovation.

The power of IoT solutions, and their impact on the global economy, are driving demand for robust and secure enterprise data warehouse capabilities. IoT presents a particular challenge on the infrastructure level because many technical requirements at scale cannot be predicted in advance. Some customers need to manage massive IoT datasets while others require real-time data streaming or fine-grained access controls.

The breadth of infrastructure requirements in the IoT space means Leverege depends on partnering with a best-in-class cloud computing provider. On the technical side, a full-featured data warehouse is required to meet customer needs and bring them to scale. On the financial side, the end-to-end solution must be designed to manage and reduce overall costs, accounting for each of the solution’s components (hardware, connectivity, infrastructure, and software).

By leveraging the scalability and flexibility of Google Cloud Platform and BigQuery, Leverege’s customers can affordably store, process, and analyze data from millions of connected devices and extract the value they need from sensor data.

Introduction to Leverege using Google Cloud

Leverege offers a customizable multi-layer IoT stack to help organizations quickly and easily build and deploy IoT solutions that provide measurable business value. The Leverege IoT Stack consists of three components:

Leverege Connect is focused on device management, enabling the secure provisioning, connection and management of distributed IoT devices. Leverege Connect serves as a replacement for Google IoT Core which will be retired in August 2023 and supports protocols such as MQTT, HTTP, UDP, and CoAP.
Leverege Architect is focused on data management, enabling the ingestion, organization, and contextualization of device and business data with the ability to apply AI/ML for powerful insights and/or expose via APIs to external services.
Leverege Build optimizes application development, enabling the generation, configuration, and branding of end-user applications with tailored experiences on a per-role basis; all with no-code tooling.

Leverege IoT Stack is deployed with Google Kubernetes Engine (GKE), a fully managed kubernetes service for managing collections of microservices. Leverege uses Google Cloud Pub/Sub, a fully managed service, as the primary means of message routing for data ingestion, and Google Firebase for real-time data and user interface hosting. For long-term data storage, historical querying and analysis, and real-time insights , Leverege relies on BigQuery.

Leveraging BigQuery to deliver and manage IoT solutions at scale

Use case #1: Automating vehicle auctions for the world’s largest automobile wholesaler

The world’s leading used vehicle marketplace faced the costly challenge of efficiently orchestrating and executing simultaneous in-person and online car auctions on parking lots up to 600 acres in size. Before the IoT solution was deployed, manually staging thousands of vehicles each day involved hundreds of people finding specific vehicles based on hard-to-discover information and attempting to arrange them in precise order. This manual process was highly inefficient, unreliable, and negatively impacted the customer experience since vehicles routinely missed the auction or were out of sequence.

To solve the problem, the customer built low-cost, long battery life GPS trackers and placed them inside all of the vehicles on the lot. Leverege integrated the devices into a holistic end-to-end solution, providing full awareness and visibility into precise car location, diagnostics, automated querying, analysis reports, and movement with walking directions to vehicles of interest. This digital transformation saved the customer millions of dollars a year while simultaneously increasing customer satisfaction by a significant amount.

After the solution scaled nationwide, monitoring the health of the devices and system was paramount for operational success. BigQuery data partitioning and autonomous analysis jobs allowed for a cost effective way to manage and segment system alerts and reports of overall system health using very large datasets.

Use case #2: Analyzing the state and readiness of boats anywhere in the world in real-time

Working with the largest boat engine manufacturer in the world, Leverege delivered an IoT solution providing boat owners and fleet managers with real-time, 24/7 access to the state, readiness, and location of their boats around the globe.

Seamlessly and reliably providing real-time marine data to boat owners requires technical integration across hardware, software, and connectivity, a problem uniquely suited for an IoT solution. The customer’s “Connected Boat” product reports a high volume of disparate data including the status of every electrical, mechanical, and engine subsystem. Some of this data is only important historically when incidents and issues arise and boat owners need to investigate.

BigQuery allows Leverege to record the full volume of historical data at a low storage cost, while only paying to access small segments of data on-demand using table partitioning.

For each of these examples, historical analysis using BigQuery can help identify pain points and improve operational efficiencies. They can also do so with both public datasets and private datasets. This means an auto wholesaler can expose data for specific vehicles, but not the entire dataset (i.e., no API queries). Likewise, a boat engine manufacturer can make subsets of data available to different end users.

Leverege IoT Stack reference architecture: Integrating components to deliver robust, scalable, and secure solutions

The Leverege IoT Stack is built on top of Google Cloud's infrastructure, making use of several core components that work together to deliver a robust, scalable, and secure solution. These components include:

GKE: Leverege uses GKE to deploy a collection of microservices and easily scale end-to-end IoT solutions. These microservices handle tasks such as device management, data ingestion, and real-time data processing. In addition, GKE secures high degree of business continuity, enables self-healing and fault tolerance, which allow Leverege to provide enterprise-grade availability and uptime. These capabilities are crucial for Leverege to meet requirements specified by Service-Level Agreements.
Pub/Sub: Leverege uses Pub/Sub to orchestrate the routing of messages for data ingestion, allowing customers to process data in near real-time. This provides a highly auto scalable, fault-tolerant message queuing system.
Firebase: Leverege uses Firebase for real-time data and UI hosting, providing customers with a responsive and interactive user experience. With Firebase, customers can easily access and visualize IoT data, as well as build and scale applications with minimal effort.

BigQuery: BigQuery is a fundamental part of the Leverege solution. It enables customers to run long-term data storage and complex, historical SQL-like queries. These queries can be run on large amounts of data in real-time, providing customers actionable insights that can help improve operational efficiencies.

Solution: Leveraging core BigQuery features for IoT use cases

Many technology companies make extensive use of specific BigQuery features to deliver business-critical outcomes. Some use cases demand sub-second latency; others require adaptable ML models. By contrast, enterprise IoT use cases typically include a broad set of requirements necessitating the use of the full breadth of BigQuery’s core features. For example, Leverege uses an array of BigQuery features, including:

Data Storage: BigQuery serves as a limitless storage platform allowing Leverege customers to store and manage large-scale IoT data with high availability, including real-time and historical data. Some of Leverege’s integrated devices can report thousands of times a day. At a scale of millions of devices, Leverege’s customers need a scalable data warehouse.
Real-Time Streaming: BigQuery also provides a powerful streaming capability, which allows the Leverege IoT Stack to ingest and process large amounts of data in near real-time. This is crucial to components of Leverege Build, which offers out-of-the-box charts and graphs using historical data. These tools are more valuable with the integration and use of real-time data. Streaming capabilities ensure customers easily access full-scope data without searching Google Firebase.
Data Partitioning: BigQuery enables cost-effective, fast queries by providing customizable data partitioning. The Leverege IoT stack partitions nearly all historical tables by ingestion time. Because most internal history queries are time-based, this results in significant cost savings.
Data Encryption: BigQuery provides built-in encryption at rest by default, allows customers to securely store sensitive data and protect it against unauthorized access.
Access Control: BigQuery provides numerous secure data sharing capabilities. Leverege uses linked data sets and authorized views with row level policies to enforce strict access control. These policies are critical because many IoT projects allow for multi-tenancy and data siloing.
Data Governance: BigQuery provides a robust set of data governance and security features, including fine-grained access controls, which Leverege uses to enforce intricate access control policies down to the row level.

In addition to BigQuery’s core features, Leverege uses BigQuery Analytics Hub private data exchanges and Authorized Views on Datasets provides distinct advantages over old methods (e.g. CSV exports and FTP drops). Authorized Views on Leverege's BigQuery datasets allow for intricate access policies to be enforced, while also providing Leverege’s customers the ability to analyze data using tools like Looker. Using these BigQuery features, Leverege can give customers controlled and metered access to source data without providing direct access. This feature is fundamental to meeting governance requirements across the enterprise.

BigQuery's built-in machine learning capabilities also allow for advanced analysis and prediction of trends and patterns within the data, providing valuable insights for our customers without moving the data to external systems. Furthermore, the ability to set up automatic data refresh and materialized views in BigQuery ensures that our customers are always working with the most up-to-date and accurate data by getting better performance and reducing unnecessary costs.

Benefits and outcomes

Google Cloud infrastructure and BigQuery features enable Leverege to provide a highly scalable IoT stack. In IoT, the central challenge isn’t deploying small-scale solutions; it’s deploying and managing large-scale, performative solutions and applications by scaling in a short span of time without rearchitecting.

BigQuery table partitioning splits data into mini tables divided by an arbitrary time range. For many Leverege customers, data is divided by day, and enforced when querying data through the Leverege IoT Stack. Partitioning data tables by time range guarantees queries are restricted to a small subset of data falling within the targeted time range. By using partitioning, Leverege can deliver a performant solution at minimal cost.

BigQuery clustering further enhances performance by splitting data into designated fields. To make queries more efficient, Leverege uses clustering to query data that meet pre-designated filter criteria. In a large-scale solution with 100,000 devices, Leverege can cluster data tables and query the history of single devices, greatly accelerating searches and making the system much more performant. In addition, the reclustering happens seamlessly in the background without any extra costs.

The integration of the Leverege IoT Stack and Google Cloud, including BigQuery, today power business-critical enterprise IoT solutions at scale. The continued rapid pace of development on the infrastructure and application levels will be essential in delivering the next generation of IoT solutions.

Click here to learn more about Leverege’s capabilities or to request a demo.

The Built with BigQuery advantage for ISVs

Google is helping tech companies like Leverege build innovative applications on Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs through the Built with BigQuery initiative, launched in April as part of the Google Data Cloud Summit. Participating companies can:

Get started fast with a Google-funded, pre-configured sandbox.
Accelerate product design and architecture through access to designated experts from the ISV Center of Excellence who can provide insight into key use cases, architectural patterns, and best practices.
Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

BigQuery gives ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in.

Click here to learn more about Built with BigQuery.

^{We thank the Google Cloud and Leverege team members who co-authored the blog: Leverege: Tony Lioon, Director, DevOps. Google: Sujit Khasnis, Solutions Architect & Adnan Fida, Transformation Technical Lead}

Google Cloud to join Catena-X and help build a sovereign data ecosystem in the automotive industry

Mon, 06 Feb 2023 17:00:00 +0000

The automotive industry has a long history of being at the forefront of automation and digitalization in many areas, be it digital design or digital shop floor. In parallel, carmakers have grown into broad partnerships within their ecosystem, with a special focus on their suppliers in the value chain.

Digitizing and automating the way partners across the value chain work with each other and establishing a more advanced way of exchanging data based on digital sovereignty and open standards is an obvious evolution for this industry. This is the pioneering effort driven by Catena-X.

Catena-X is building a trustworthy, collaborative, open, and secure data ecosystem to enable end-to-end data chains for core automotive business processes. For the first time, the entire automotive value chain — from raw material suppliers to original equipment manufacturers (OEMs) and end-of-life partners, like recyclers — is collaborating globally with software and solution providers to create a shared service ecosystem.

In this unique data space, every partner has sovereign control over its data and can collaborate with respective market participants via an open and interoperable solution portfolio. The result will be more resilient and flexible value chains with better visibility on items like raw material sources and CO2 footprint, creating opportunities for partner companies to decarbonize their value chains. Based on open source, this data space will offer a unique breeding ground for open innovation and collaborative approaches for the entire industry.

Google Cloud is proud to join the Catena-X Automotive Network Association. We bring to the community our best-in-class secure and sovereign data management, data analytics and native integration of advanced AI technologies, and open source efforts. All will help Catena-X succeed in its mission, and with that help our allies in the industry in their journey to become data-driven businesses.

“Google Cloud’s membership in Catena-X will support global adoption and will strengthen our capabilities in collaboratively building a data space based on open source,” said Oliver Ganser, Chairman of the Board of Catena-X Automotive Network.

How can Google Cloud assist? Here are a few examples:

End-to-end automotive supply chain: Google Cloud technology helps companies improve demand and capacity management by connecting and joining data sources of private, community, and public data. This results in improved real-time insights, better results, massive efficiency improvements, anticipation and mitigation of risk, and cost reductions. This also enables businesses to digitize their supply chain and build capabilities to operate a more sustainable end-to-end supply chain.
Digital sovereignty: In September 2021, Google Cloud unveiled “Cloud. On Europe’s Terms,” an ambitious commitment to deliver cloud services that provide the highest levels of digital sovereignty. Data sovereignty is a key design criteria both in IDSA as well as Catena-X, which is the first real implementation of a federated and secure data space for Gaia-X. We not only offer unique capabilities for data sovereignty, but continue to advance options for increased operational and software sovereignty as well. Google Cloud brings this to market in partnership with local trusted providers including T-Systems in Germany and S3NS in France.
Eclipse Data Space Connector (EDC): A key success factor for Catena-X to connect partners across the industry is the availability of data space connectors. Google Cloud, as a member of the International Data Spaces Association (IDSA), is contributing actively to the open-source Dataspace Connector.
Sustainability built-in: Google Cloud is proud to offer the cleanest cloud in the industry, matching our energy consumption with 100% renewable energy (not just carbon credits, but real power) since 2017. We also help our customers by enabling them to leverage data analytics and AI technology to solve data challenges that can minimize carbon emission along their value chain; efforts which are closely aligned with the key priorities and use cases of Catena-X.

“By placing data, privacy and sovereignty at the center of its ecosystem, Catena-X helps its contributors maximize the value of their data. Data often already exists but is not always accessible and therefore can not readily be used to improve business outcomes,” said Dr. Wieland Holfelder, Vice President Engineering, Regional CTO for Google Cloud Security and Sovereignty.

Google Cloud is a trusted partner for the automotive industry to build and operate open data ecosystems in which modern data products can be shared seamlessly in a secure way across a “digital thread.” This end-to-end integration of data enables breaking down silos and delivering on the strategy of democratization of data. Catena-X extends the pool of data available to a single company through an ecosystem of its partners, producing a unique implementation of a data-driven value chain.

Built with BigQuery: How Oden provides actionable recommendations with network resiliency to optimize manufacturing processes

Fri, 03 Feb 2023 17:00:00 +0000

Background

The Oden Technologies solution is an analytics layer for manufacturers that combines and analyzes all process information from machines and production systems to give real-time visibility to the biggest causes of inefficiency and recommendations to address them. Oden empowers front-line plant teams to make effective decisions, such as prioritizing resources more effectively, solving issues faster, and realizing optimal behavior.

Use cases: Challenges and problems solved

Manufacturing plants have limited resources and would like to use them optimally by eliminating any inefficiencies and making recommendations and providing data points as a key input for decision making. These data points are based on a torrent of data coming from multiple devices.

Oden’s customers are manufacturers with continuous and batch processes, such as in plastics extrusion, paper and pulp, and chemicals. Oden powers real-time and historical dashboards and reports necessary for this decision-making through leveraging the underlying Google Cloud Platform.

Oden’s platform aggregates streaming, time-series data from multiple devices and instruments and processes them in real-time. This data is in the form of continuously sampled real-world sensor readings (metrics) that are ingested into CloudIoT and transformed in real-time using Dataflow before being written to Oden's time series database. Transformations include data cleaning, normalization, synchronization, smoothing, outlier removal, and multi-metric calculations that are built in collaboration with manufacturing customers. The time-series database then powers real-time and historical dashboards and reports.

One of the major challenges of working with real-time manufacturing data is handling network disruptions. Manufacturing environments are often not well served by ISPs and can experience network issues due to environmental and process conditions or other factors. When this happens, data can be backed up locally and arrive late after the connection recovers. To avoid overloading real-time dataflow jobs with this late data, BigQuery supports late data handling and recoveries.

In addition to the sensor data, Oden collects metadata about the production process and factory operation such as products manufactured on each line, their specifications and quality. Integrations provide the metadata via Oden’s Integration APIs running on Google Kubernetes Engine (GKE), which then writes it to a PostgreSQL database hosted in CloudSQL. The solution then uses this metadata to contextualize the time-series data in manufacturing applications.

Oden uses this data in several ways, including real-time monitoring and alerting, dashboards for line operators and production managers, historical query tools for quality engineers, and machine learning models trained on historical data and scored in real-time to provide live predictions, recommendations, and insights. This is all served in an easy to access and understand UI, greatly empowering employees across the factory to use data to improve their lines of business.

The second major challenge in manufacturing systems, is achieving quality specifications on the final product for it to be sold. Typically, Quality Assurance is conducted offline: after production has completed, a sample is taken from the final product, and a test is performed to determine physical properties of the product. However, this introduces a lag between the actual time period of production, and information about the effectiveness of that production—sometimes hours (or even days) after the fact. This prevents proactive adjustments that could correct for quality failures, and results in considerable waste.

Solution architecture

At the heart of the Oden platform is Google BigQuery, which plays an important backstage role in Oden’s data-driven software. Metric data is written simultaneously to BigQuery via a BigQuery Subscription through Cloud PubSub and metadata from CloudSQL is accessible via BigQuery’s Federated Queries. This makes BigQuery an exploratory engine for all customer data allowing Oden’s data scientists and engineers to support the data pipelines and build Oden’s machine learning models.

Sometimes these queries are ad-hoc analysis that helps understand data better. For example, here’s a BigQuery query joining both the native BigQuery metrics table and a Federated Query to the metadata in PostgreSQL This query helps determine the average lag between the event time and ingest time of customer metrics by day for November:

code_block: <ListValue: [StructValue([('code', 'SELECT\r\n customer_id,\r\n metric_id,\r\n TIMESTAMP_TRUNC(ingest_time, DAY) AS ingest_day,\r\n AVG(TIMESTAMP_DIFF(ingest_time, ingest_time, MILLISECOND)) AS diff_ms\r\nFROM\r\n `oden-production.metrics.metrics`\r\nJOIN\r\n EXTERNAL_QUERY("oden-production.us.metadatadb",\r\n "SELECT customer_id::text, metric_id::text FROM customer_metrics")\r\nWHERE\r\n ingest_time >= \'2022-11-01\'\r\n AND ingest_time < \'2022-12-01\'\r\nGROUP BY\r\n customer_id,\r\n metric_id,\r\n ingest_day'), ('language', ''), ('caption', <wagtail.rich_text.RichText object at 0x7f64741a3310>)])]>

In addition to ad-hoc queries, there are also several key features of Oden that use BigQuery as their foundation. Below, two major features that leverage BigQuery as the highly scalable source of truth for data are covered.

Use case 1: The data reliability challenge of manufacturing and the cloud

As mentioned earlier, one of the major challenges of working with real-time manufacturing data is handling network disruptions. After the connection recovers, you encounter data that has been backed up and is out of temporal sequence. To avoid overloading real-time dataflow jobs with this late data, BigQuery is used to support late data handling and recoveries.

The data transformation jobs that run on Dataflow are written in the Apache Beam framework and usually perform their transformations by reading metrics from an input Pubsub topic and writing back to an output topic. This forms a directed acyclic graph (DAG) of transformation stages before the final metrics are written to the time-series database. But the streaming jobs degrade in performance when handling large bursts of late data putting the ability at risk to meet Service Level Agreements (SLAs), which guarantee customers high availability and fast end-to-end delivery of real-time features.

A key tenet of the Apache Beam model is that transformations can be applied to both bounded and unbounded collections of data. With this in mind, Apache Beam can be used for both streaming and batch processing. Oden takes this a step further with a universal shared connector for every one of the transformation jobs which allows the entire job to switch between a regular “streaming mode” and an alternative “Batch Mode.” In “Batch Mode,” the streaming jobs can do the same transformation but use Avro files or BigQuery as their data source.

This “Batch Mode'' feature started as a method of testing and running large batch recoveries after outages. But it has since evolved into a solution to late data handling problems. All data that comes in “late” to Oden bypasses the real-time Dataflow streaming jobs and is written to a special “Late Metrics” PubSub topic and then to BigQuery. Nightly, these “Batch Mode” jobs are deployed and data is queried that wasn’t processed that day out of BigQuery and results written back to the time-series database. This creates two SLAs for customers; a real-time one of seconds for “on-time” data and a batch one of 24 hours for any data that arrives late.

Occasionally, there is a need to backfill transformations of these streaming jobs due to regressions or new features to backport over old data. In these cases, batch jobs are leveraged again. Additionally, jobs specific to customer data are joined with metrics and customer configuration data hosted in CloudSQL via BigQuery’s Federated queries to CloudSQL.

By using BigQuery for recoveries, dataflow jobs continue to run smoothly, even in the face of network disruptions. This allows maintaining high accuracy and reliability in real-time data analysis and reporting. Since moving to separate BigQuery-powered late-data handling, the median system latency of calculated metrics feature for real-time metrics is under 2s which allows customers to observe and respond to their custom multi-sensor calculated metrics instantly.

Use case 2: Building and delivering predictive quality models

The next use case deals with applying machine learning to manufacturing: predicting offline quality test results using real-time process metrics. This is a challenging problem in manufacturing environments, where not only is high accuracy and reliability necessary, but the data is also collected at different sampling rates (seconds, minutes, and hours) and stored in several different systems. The merged datasets represent the comprehensive view of data to factory personnel, who use the entire set of context information to make operational decisions. This ensures Predictive Quality models access this same full picture of the manufacturing process as it provides predictions.

At Oden, BigQuery addresses the two key challenges of machine learning in the manufacturing environment:

Using time series data stored in time series database, summary aggregates are performed to construct features as input for model training and scoring.
Using federated queries to access context metadata, data is merged with the aggregates to fully characterize the production period. This allows easily combining the data from both sources and using it to train machine learning models.

Oden uses a variety of models and embeddings — ranging from linear models (Elastic Nets, Lasso), ensemble models (boosted trees, random forests) to DNNs that allow addressing the different complexity-accuracy-interpretability requirements of customers.

The chart shows out-of-sample predictions of offline quality test values, compared with the actual values that were observed after the end of production. The predicted values provide lead time of quality problems of up to one hour.

Models are trained using an automated pipeline based on Apache Airflow and scikit learn, and models are stored in Google Cloud Storage for versioning and retrieval. Once the models are trained, they can be used to predict the outcomes of quality tests in real-time via a streaming Dataflow job. This allows factory floor operators to identify and address potential problems before they become more serious or have a large impact. This improves the overall efficiency of the production process, and reduces the amount of waste that a factory generates. Factory floor operators receive up-to-date information about the quality characteristics of current production conditions, up to an hour before the actual test value is available for inspection. This gives early warning to help catch quality failures. In turn, this reduces material waste and machine downtime, metrics that are central to many manufacturers’ continuous improvement initiatives, as well as their day-to-day operations.

Operators come to rely upon predictive models to execute their roles effectively, regardless of their experience level or their familiarity with a specific type of production or machinery, and up-to-date models are critical to the success of the predictions in improving manufacturing processes. Hence, in addition to training, life-cycle management of models and ML ops are important considerations in deploying reliable models to the factory floor. Oden is focusing on leveraging Vertex AI to make the ML model lifecycle more simple and efficient.

Oden’s Predictive Quality model empowers operators to take proactive steps to optimize production on the factory floor, and allows for real-time reactions to changes in the manufacturing process. This contributes to cost reduction, energy savings, and reduced material wasted.

The future of BigQuery at Oden

Actionable data, like the processed data generated by Oden, has become such a critical part of making predictions and decisions to remain competitive in the manufacturing space. In order to use these insights to their full potential, businesses need a low barrier to access data, unify the data with other data sources, derive richer insights and make learned decisions. Oden already leads the market in having trustworthy, usable, and understandable insights from combined process, production, and machine data that is accessible from everyone within the plant to improve their line of business. There is opportunity to go beyond the Oden interface to integrate with even more business systems The data can be made available in the form of finished datasets, hosted in BigQuery. Google Cloud has launched a new service called Analytics Hub , powered by BigQuery with the intent to make data sharing easier, secure, searchable, reliable and highly scalable.

Analytics Hub is based on the Publish-Subscribe model where BigQuery datasets are enlisted into a Data exchange as a Shared dataset, which hosts hundreds of listings. It lets users share multiple BigQuery objects such as views, tables, external tables, models etc into the Data exchange. A Data exchange can be marked public or private for a dedicated sharing. On the other end, businesses can subscribe to one or more listings in their BigQuery instance, where it is consumed as a Linked dataset to run queries against. Analytics Hub sets up a real-time data pipeline with a low-code no-code approach to share data, while giving Oden complete control over what data needs to be shared for better governance.

This empowers advanced users, who have use-cases that exceed the common workflows already achievable with Oden’s configurable dashboard and query tools, to leverage the capabilities of BigQuery in their organization. This brings Oden’s internal success with BigQuery directly to advanced users. With BigQuery, they can join against datasets not in Oden, express complex BigQuery queries, load data directly with Google’s BigQuery client libraries, and integrate Oden data into third party Business Intelligence software such as Google Data Studio.

Better together

Google Cloud and Oden are forging a strong partnership in several areas, most of which are central to customers needs. Oden has developed a turnkey solution by using the best in class Google Cloud tools and technologies, delivering pre-built models to accelerate time to value and enabling manufacturers to have accessible and impactful insights without hiring a data science team. Together, Google and Oden are expanding the way manufacturers access and use data by creating a clear path to centralize production, machine, and process data into the larger enterprise data platform paving the way for energy savings, material waste reduction and cost optimization,

Click here to learn more about Oden Technologies or to request a demo.

The Built with BigQuery advantage for ISVs

Google is helping tech companies like Oden Technologies build innovative applications on Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs through the Built with BigQuery initiative, launched in April ‘22 as part of the Google Data Cloud Summit. Participating companies can:

Get started fast with a Google-funded, pre-configured sandbox.
Accelerate product design and architecture through access to designated experts from the ISV Center of Excellence who can provide insight into key use cases, architectural patterns, and best practices.
Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.

Click here to learn more about Built with BigQuery.

^{We thank the Google Cloud and Oden team members who co-authored the blog: Oden: Henry Linder, Staff Data Scientist & Deepak Turaga, SVP Data Science and Engineering. Google: Sujit Khasnis, Solutions Architect & Merlin Yammsi, Solutions Consultant}