Google Cloud Consulting

Build financial resilience with AI-powered tabletop exercises on Google Cloud

Wed, 11 Feb 2026 17:00:00 +0000

In the financial sector, resilience isn't optional. Recent cloud outages have shown us exactly how fast critical data can disappear.

The risk is amplified by major regulatory drivers like the Digital Operational Resilience Act (DORA), which mandates that financial institutions are ready for any disruption. The recent designation of Google Cloud as a Critical Third-Party Service Provider (CTPP) under DORA further underscores this strong commitment to enabling secure and resilient financial operations for our customers.

Consider a major bank, with many critical apps processing thousands of transactions daily. For them, a critical incident means more than just downtime; it means regulatory fines and an erosion of client trust.

The problem: Traditional tabletop exercises fail in two key ways:

They rely on generic scenarios that do not reflect the complexity and unique weaknesses of the institution's actual production environment.
They often involve only IT or Compliance teams, failing to capture the cross-functional collaboration essential for real-world incident response.

In this blog, we’ll share a solution using context-aware scenario modeling on Gemini Enterprise.

Our solution? Context-aware scenario modeling powered by Google AI

Google Cloud’s Technical Account Management (TAM) team has pioneered a new approach to operational resilience testing that moves beyond textbook scenarios.

Our innovation is making these exercises dynamic and truly context-aware. Our team dives deep, ingesting and analyzing the customer’s actual operational information from different sources to build deeply customized, realistic scenarios. We look at everything from past support cases and meeting minutes to the application’s architecture and even billing metrics and SLAs.

Then, we use Gemini Enterprise to design a bespoke scenario, complete with a step-by-step timeline of exactly what should fail and when, plus the mitigation checks needed. Context-aware AI preparation can help FSI customers test their resilience against situations rooted directly in their own production environment, supporting their response strategy is fit for purpose.

High-level sample scenario timeline

To illustrate the realistic, evolving scenario designed by Gemini Enterprise, here is a high-level snapshot of a simulated incident. Note - the metrics are for illustrative purposes only.

Time (hh:mm)	Phase / Action	Customized context-aware event
T + 0:00	Initial anomaly	A spike in latency is detected on a critical transactional processing service. Logs show unusual API calls.
T + 0:15	Escalation / Discovery	Application operations reports that the primary customer database is showing a 150% increase in read errors.
T + 0:45	Critical impact	The App team confirms the errors are due to data corruption in a critical region. Regulatory alert issued.
T + 1:15	Remediation attempt	An Incident Manager attempts to execute the standard failover runbook, but the attempt fails due to a known, outdated configuration issue.
T + 2:00	Crisis point	Internal communication systems become slow due to load, forcing the team to use alternative means of communication (Testing communications protocol).

The simulated incident progression, from a specific service anomaly to a regulatory crisis point, is deeply informed by the customer’s actual environment and documented weaknesses, making the exercise intensely relevant.

The execution: A cross-functional emergency drill

In a recent simulation with a major FSI customer, this approach uncovered a dual critical incident involving massive latency and data corruption — a perfect stress test for their core systems.

The result was a real-time, interactive emergency drill that brought together the full spectrum of business decision makers. The diversity of participants was key to exposing gaps across technology, process, and communication.

The cross-functional simulation strategy led to a high-fidelity discussion, helping the customer uncover blind spots and refine its emergency response strategy in a safe, yet realistic, setting.

Impact & key results

We’ve successfully executed this AI-driven approach with large FSI customers across the DACH (Germany, Austria, Switzerland) region, and the impact has been immediate and measurable:

Practical steps: The exercise highlighted organizational strengths (e.g., cross-functional communication) and led directly to high-priority initiatives (e.g., implementing specific automated failover runbooks). Crucially, nearly all suggestions were quickly implemented because they were grounded in a real production-like scenario.
A shift in strategy: The realism was so impactful that many customers are now actively looking into integrating AI-based threat modeling into their existing compliance processes.
A lasting partnership: The AI-powered tabletop program, high-value service is now scheduled as a regular exercise with the Technical Account Managers, solidifying Google Cloud Consulting as a strategic collaborator in the customer's operational resilience journey.

Get started

Interested in moving beyond generic disaster drills and truly validating your organization's resilience?

Reach out to your Google TAM today to learn how our AI-powered tabletop exercises can validate your readiness.

From legacy to cloud: How Deutsche Telekom went from PySpark to BigQuery DataFrames

Thu, 14 Aug 2025 16:00:00 +0000

In today's hyper-competitive telecommunications landscape, understanding and maximizing the Customer Lifetime Value (CLV) metric isn't just a nice-to-have, it's a strategic imperative. For Deutsche Telekom, accurate CLV calculations are the bedrock of informed decisions, driving crucial initiatives in customer acquisition, retention, and targeted marketing campaigns. The ability to predict and influence long-term customer relationships directly translates to sustained profitability and a competitive edge.

Initially, Deutsche Telekom's Data Science team processed data within an on-premises data lake environment, leveraging Jupyter notebooks and PySpark. However, this reliance on legacy on-prem data lake systems was creating significant bottlenecks. These systems, designed for a different era of data volume and complexity, struggled to handle the massive datasets required for sophisticated CLV modeling. The result? Extended processing times, limited agility in data science experiments, and a growing gap between potential insights and actionable results.

This challenge demanded a transformative solution, leading Deutsche Telekom to embrace the power of modern cloud infrastructure, specifically Google Cloud's BigQuery, to unlock the full potential of their data and accelerate their journey towards data-driven innovation.The core of this transformation was the migration of critical data science workloads, beginning with the CLV calculations, to BigQuery.

Deutsche Telekom decided that for distributed Python data processing, they wanted to move off of PySpark-based code and adopt BigQuery DataFrames a.k.a. BigFrames. BigFrames is an open-source Python library offered by Google that scales Python data processing by transpiling common Python data science APIs to BigQuery SQL. You can read more about BigFrames in the official introduction to BigFrames and can refer to the public git repository.

This decision was driven by three factors:

Keep it simple: By moving all the data processing to BigQuery, the company would be standardizing on a single data processing technology. This helps in administration and standardization across the organization.
Bet on universal skills: Python and Pandas are universal data science skills, but bringing in Spark would introduce additional learning. BigFrames is Pandas-like — it pushes processing to BigQuery. The move reduces the upskilling required to work on data science tasks.
Focus on business logic: Data science teams can focus on core business logic and less on the infrastructure required to make that logic work.

In other words, this move was not just a technical upgrade — it was a strategic shift towards a more agile, efficient, and insight-driven future. By leveraging BigQuery's ability to process massive datasets quickly and efficiently, along with the tight integration of BigQuery DataFrames and its compatibility with familiar pandas and scikit-learn APIs, Deutsche Telekom aimed to eliminate the bottlenecks that had hindered their data science initiatives. This solution, centered around BigQuery and BigQuery DataFrames, provided the foundation for faster insights, improved decision-making, and ultimately, enhanced customer experiences.

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The migration journey

To realize these benefits, Deutsche Telekom meticulously planned a two-phase technical migration strategy, designed to minimize disruption and maximize the speed of achieving tangible business results.

The actual effort required to execute the two phases above was one person-week. Conversion with Gemini was 95% accurate and worked as expected. In the first phase, the most time was spent on manual data validation. Furthermore, around 70% of the pandas code automatically functioned as BigQuery DataFrames code as well. Some adjustments were required for data types and scalar functions.

Phase 1: Accelerated transition with AI-powered code conversion
The initial phase focused on rapidly converting existing code to a format compatible with Google Cloud's environment. This was significantly accelerated by leveraging advanced AI tools like Gemini, which streamlined the code conversion process. This allowed the team to focus on validating results and ensuring business continuity, rather than getting bogged down in lengthy technical rewrites.

Phase 2: Optimizing for cloud scalability and performance
The second phase involved adapting the data processing to fully leverage BigQuery. This step was crucial for eliminating the performance bottlenecks they had experienced with the legacy systems. By aligning the data processing with BigQuery's capabilities, Deutsche Telekom was able to unlock significant improvements in processing speed and scalability, allowing for faster and more insightful data analysis.

Key business benefits of the technical approach:

Deutsche Telekom’s technical migration strategy was not just about moving data; it was about strategically enabling faster, more scalable, and more reliable data-driven decisions. Among the benefits that they saw from this approach were:

Faster time to insight: The accelerated code conversion, powered by AI, significantly reduced the time required to migrate and validate the data, enabling quicker access to critical business insights.
Improved scalability and performance: The transition to BigQuery's cloud-native architecture eliminated performance bottlenecks and provided the scalability needed to handle growing data volumes.
Reduced operational risk: The structured, two-phase approach minimized disruption and ensured a smooth transition, reducing operational risk.
Leveraging existing expertise: The use of familiar tools and technologies, combined with AI-powered assistance, allowed the team to leverage their existing expertise, minimizing the learning curve.

Of course, a project of this scale presented its own set of unique challenges, but each one was addressed with solutions that further strengthened Deutsche Telekom’s data capabilities and delivered increased business value.

Challenge 1: Ensuring data accuracy at scale
Initially, the test data didn't fully represent the complexities of their real-world data, potentially impacting the accuracy of critical calculations like CLV.

Solution: During the test phase, they relaxed the filters on the data sources to overcome the data size problem. They implemented the changes in both old and new versions of the code to reliably compare the outputs.

Challenge 2: Maintaining robust security and compliance
Balancing the need for data access with stringent security and compliance requirements was an important consideration. BigQuery DataFrames documentation highlights the need for admin-level IAM privileges for some tasks like Remote Functions, which may not be possible in enterprise environments.

Solution: Deutsche Telekom developed customized IAM roles that met its security standards while enabling data access for authorized users. This helped ensure data security and compliance while supporting business agility.

By addressing these challenges strategically, Deutsche Telekom not only had a successful migration, but it also delivered tangible business benefits. Deutsche Telekom now has a more agile, scalable, and secure data platform, enabling them to make faster, more informed decisions and ultimately enhance their customer experience. This project demonstrates the power of cloud transformation in driving business value.

Deutsche Telekom's successful migration to BigQuery was a strategic transformation, not just a technical one. By overcoming the limitations of their legacy systems and embracing cloud-based data processing, they've established a robust foundation for future innovation. This project underscores the power of strategic partnerships and collaborative problem-solving, showcasing how Google Cloud's cutting-edge technologies and expert consulting can empower businesses to thrive in the data-driven future.

Ready to unlock the full potential of your data?

Whether you're facing similar challenges with legacy systems or seeking to accelerate your data science initiatives, Google Cloud's data platform can provide the solutions you need. Explore the capabilities of BigQuery and DataFrames, and discover how our expert consultants can guide you through a seamless cloud migration.

Contact Google Cloud Consulting today to discuss your specific needs and embark on your own journey towards data-driven innovation.

_{A special thanks to Googler Rohit Naidu, Strategic Cloud Engineer, for his contributions to this post.}

Autonomous Network Operations framework: Unlock predictable and high-performing networks

Thu, 12 Jun 2025 13:00:00 +0000

Over the past year, an exponential surge in data, the widespread rollout of 5G, and heightened customer expectations have placed unprecedented demands upon communications service providers (CSPs). To thrive in this challenging landscape, telecommunications leaders are rethinking traditional network management, embracing digital transformation, and using the power of AI to build smarter, more efficient, and self-managing networks.

Today, to help CSPs address these pressures, we are announcing the Autonomous Network Operations framework — enabling CSPs to enhance service reliability, proactively detect and resolve network issues, and turn fragmented data into value. This new framework takes an AI-first approach, leveraging the latest in Google Cloud AI, infrastructure, and analytics products to understand and make sense of complex network data, risks, and operations. The framework also offers an extensive ecosystem to help deploy these solutions, including partners and Google Cloud Consulting.

The Autonomous Network Operations framework draws on Google’s extensive expertise in operating its own global network, which has leveraged AI at scale for more than 25 years and is one of the industry’s most advanced and resilient autonomous networks. CSPs are already using the framework to improve service reliability and minimize mean time to repair (MTTR) by 25%, and now we’re making it broadly available to accelerate their autonomous network operations journeys.

Navigating network complexity in the AI era

Managing complex telecom networks is a costly and resource-intensive undertaking for CSPs. Legacy infrastructure, often built for previous generations of mobile technology, struggles to keep pace with the immense data demands of 5G and beyond. This has led to several challenges:

Increased operational costs and network demands: Manual tasks like alarm triage, troubleshooting, configuration, and service provisioning across diverse systems consume significant resources for CSPs, hindering innovation and modernization.
Sub-optimal customer experience: Network operations traditionally focus on technical KPIs (utilization, latency, etc.) without real-time visibility into how specific network events or degradations are actually affecting the quality of experience for individual subscribers or services. Taking action usually happens reactively, leading to eroded customer satisfaction and increased churn.
Fragmented and siloed data: In many legacy platforms, vital network-performance and customer-experience data reside in separate systems, and often are difficult to integrate. But without a unified view, correlating network events with their impact on the customer experience is hard, and can lead to ineffective resource prioritization and delayed root cause analysis.
Difficulty implementing advanced technologies: Adopting autonomous networking operations can be resource intensive and costly, presenting challenges such as integration with existing infrastructure, data management, cybersecurity, upskilling talent, and identifying a clear path to a positive return on investment.

Yet, we've heard from our customers that successfully embracing autonomous network operations has the potential to dramatically improve service uptime for subscribers, significantly reduce network complexity, and unify fragmented data for actionable insights.

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Google Cloud’s Autonomous Network Operations framework

Google Cloud's Autonomous Network Operations framework supports CSPs’ strategic pathways to achieving true network autonomy, building on our unique strengths in AI, infrastructure, and global expertise.

The framework integrates critical Google Cloud products to transform operations, enhance service reliability, and unlock new value in three key ways:

1. Differentiated building blocks for sophisticated use cases

Google Cloud uniquely helps CSPs build intelligent networks with cutting-edge, AI-powered tools tailored to their specific needs. At its core is Cloud Spanner, Google Cloud’s globally distributed database that acts like a real-time virtual copy (a "digital twin") of national networks. Spanner tracks billions of changing data points across all network components, providing a single, reliable record that even retains historical network conditions. This is crucial for advanced analysis and quickly pinpointing the root cause of issues. BigQuery then adds data analysis that can handle massive amounts of live network information. Finally, Google's Gemini, our most capable AI model available through Vertex AI, and specialized Graph Neural Network (GNN) models, deeply understand the network's complex and evolving connections.

Together, these tools let CSPs move beyond simple automation to tackle advanced autonomous network operations like finding problems before they happen, linking issues across different network areas, and making quick, precise decisions based on live information, at any scale.

2. Extensive integration to accelerate time-to-value

What truly sets Google Cloud’s framework apart is how smoothly all of its pieces fit together, reducing complexity and helping CSPs get things done much faster. Our services are designed to work hand-in-hand from the start, cutting down on time-consuming data engineering. For instance, BigQuery can directly access data in Cloud Spanner, providing a unified view of current operations and historical trends. Additionally, with BigQuery ML, CSPs can build and deploy AI models using simple SQL commands, leveraging powerful AI capabilities like Gemini through Vertex AI. This tight integration across our data storage, analytics, and AI tools allows CSPs to quickly pilot, launch, and expand their AI initiatives. The entire system is AI-ready from day one, facilitating the ingestion of live network data and even enabling automated problem resolution, unlocking value in days, not months.

3. Google’s AI and network operations expertise through Google Cloud Consulting

Google’s global network, one of the most advanced and resilient networks in the world, has leveraged AI at scale for more than 25 years. Through Google Cloud Consulting, we bring this operational expertise directly to CSPs to help them design and implement their own autonomous network operations frameworks. Our teams work closely with CSPs to tailor the framework to their environments. This includes everything from setting up data pipelines to operationalizing use cases like predictive maintenance, fault correlation, and closed-loop automation — helping to ensure rapid and reliable data activation.

To help ensure these intelligent, automated operations remain secure, we also bring in Mandiant, Google’s frontline cybersecurity team. Mandiant helps CSPs embed security by design into our framework — securing data flows, detecting adversarial AI threats, and protecting automated decision loops from compromise. With this unified approach, CSPs can scale autonomous operations with the same level of resilience, visibility, and protection that underpins Google’s own global infrastructure.

A tightly integrated, open ecosystem

We strengthened the Autonomous Network Operations framework with a deep ecosystem of leading independent software vendors (ISVs) and global system integrators (GSIs), who bring specialized expertise and solutions to accelerate CSPs' transformation journeys.

For example, Amdocs, Ericsson, and Nokia now offer their own autonomous network solutions as comprehensive offerings built on the Autonomous Network Operations framework’s capabilities, enabling their customers to easily adopt and accelerate their journey toward network autonomy. These partners bring crucial expertise in handling diverse network data from various vendors, facilitating the creation of a unified data model. This unified model is essential for building sophisticated, AI-driven automation.

“As CSPs navigate the complexities of modern networks—ranging from high operational costs to the need for enhanced resiliency and uptime—intelligent automation and the evolution to autonomous networks become essential. By leveraging Google Cloud’s AI infrastructure, our Amdocs Network AIOps solution and the network agents it includes empower CSPs to proactively manage their networks through predictive analytics, automated workflows, and closed-loop operations. This collaboration enables a transformative shift toward autonomous networks, enhancing efficiency and delivering superior customer experiences.” - Anthony Goonetilleke, group president of Technology and head of Strategy, Amdocs

“The transformation to full autonomy will shape the success of CSPs, paving the way for a transition to next-generation technologies. Ericsson and Google Cloud are committed to empowering this transformation. Our collaboration is driving a fundamental shift in how mobile core networks are built and operated on public cloud infrastructure. Ericsson and Google continue to combine their expertise on multiple fronts — technology innovation, streamlined delivery models, and, most importantly, a shared culture of relentless innovation — to empower operators in realizing their vision of autonomous networks.” -- Razvan Teslaru, head of Strategy, Cloud Software and Services, Ericsson.

“The industry needs to work together to realize the benefits of Level 4/5 autonomous networks. Nokia has a long history of meaningful innovation in network automation and applied Telco AI. We’re excited about deepening our collaboration with Google Cloud, which is already delivering tangible benefits to CSPs on their own, unique journeys to fully autonomous networks.” - Kal De, senior vice president, Product and Engineering, Cloud and Network Services, Nokia.

Complementing the ISVs, GSIs including Accenture and Capgemini act as the execution arm for the CSP, playing a pivotal role in helping create the specific autonomous networking deployments, and scaling these autonomous operations across the entire organization.

Customers embracing the framework with Google Cloud

CSPs are already transforming their operations and enhancing customer experiences with the Google Cloud AI, infrastructure, and expertise provided in the Autonomous Network Operations framework:

Bell Canada achieved a 25% reduction in customer-generated reports and increased software delivery productivity by 75%. By leveraging Autonomous Network Operations framework capabilities such as Spanner Graph to dynamically assess network relationships and changes in traffic and Google Cloud AI to identify and prioritize network issues before they escalate, Bell's new AI operations (AI Ops) solution enables faster detection and resolution of network problems, improving network performance.
Deutsche Telekom ensures high service uptime for its customers, even during peak demand, with the RAN Guardian agent built using capabilities from Google Cloud's Autonomous Network Operations framework. This RAN Guardian is a multi-agentic system that constantly analyzes key network details in real time to predict and detect anomalies. It also prioritizes network issues by combining data from monitoring, inventory, performance, and coverage. Then, it automatically implements fixes, such as reallocating resources or adjusting configurations, to keep service quality high.
Telstra and Google Cloud are also co-developing a new approach to optimizing its radio access network (RAN) with an AI-powered agent. This agent uses Telstra's network data to rapidly pinpoint incidents and detect anomalies before they impact service. This project is a key step in Telstra's ambition for an autonomous network. If successful, it will unlock a future of advanced AI capabilities, enabling dynamic RAN optimization and intelligent capacity management to deliver a more resilient and higher-performing network.
Vodafone is also at the forefront of leveraging Google Cloud for autonomous networks. Their next phase focuses on a shared agentic architecture and agent-to-agent (A2A) framework, enabling intelligent automation for everything from investment planning to operational efficiency and customer experience. This builds on their extensive AI Ops deployment, evolving with gen AI and multi-agent systems to improve first-time resolution and reduce incidents.
Customers such as MasOrange, Orange, and VMO2 have also expressed interest in leveraging advanced autonomous network capabilities to enhance their operations and customer experiences.

“By achieving a 25% reduction in customer-generated reports and boosting software delivery productivity by 75%, we're transforming our operations into a customer-centric ‘techco’ model. This lean approach, with the customer as our #1 priority, is paving the way for full network autonomy. This future-forward strategy promises not only self-healing and resilient systems but also significant cost efficiencies.” – Mark McDonald, EVP and Chief Technology Officer, Bell Canada

“Transforming our network operations is fundamental to delivering best-in-class connectivity and services. By deeply integrating Google Cloud's cutting-edge capabilities like Spanner Graph with its robust data and AI tools that we use today — such as BigQuery and Vertex AI — we will better understand network behavior and anticipate service incidents. This integration is key to achieving a truly autonomous operation in our future NOC, ensuring the best experience for MasOrange customers.” - Miguel Santos Fernández, Chief Technology Officer, MasOrange

Unlock autonomous network operations today with Google Cloud

If you are a CSP who is looking to enhance service reliability, proactively detect and resolve network issues, and turn data into value, the Autonomous Network Operations framework can help. Contact a Google Cloud account manager or explore our framework on our telecommunications industry page to learn more about starting a proof-of-concept with the Google Cloud Autonomous Network Operations framework.

Lessons from the field: What decision-makers want to know about multi-agentic systems

Tue, 10 Jun 2025 16:30:00 +0000

This year, we’ve spent dozens of hours synthesizing hundreds of conversations with CXOs across leading organizations, trying to uncover their biggest thorns when it comes to building Multi-Agent Systems (MAS).

These conversations have revealed a clear pattern: MAS is helping enterprises re-think clunky legacy processes, but many CXOs are focused on automating those legacy processes rather than reimagining them. Plus, ethical risks are front and center – how do you balance innovation and ethical planning? How do CXOs take advantage of everything that’s available now, without uprooting their entire organization?

Today, we’ll explore some common missteps in the field, top questions executives have, and insights to move forward on adopting MAS today.

Quick recap: What’s the value of MAS?

MAS involves teams of coordinated AI agents working together to achieve multifaceted business goals. For example, when resolving complex customer issues, specialist agents (such as billing, usage, promotions) are managed by a coordinator agent. This orchestrator ensures that the overall resolution is driven by business logic and aligns with enterprise policies.

MAS is now transitioning from a conceptual promise to practical application. In contact centers, an orchestrator agent can analyze complex, multi-part customer queries and dynamically engage the right specialists, along with validation agents to ensure accuracy and compliance. This approach significantly improves first-contact resolution for intricate issues and increases call containment, thereby reducing the need to escalate to live agents.

Similar collaborative agent strategies are emerging across industries, such as supply chain optimization and complex research, which demonstrate MAS’s power to handle complexity through coordinated, intelligent action.

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3 common missteps from the field

Misstep 1: Automating old processes instead of reimagining them

Applying MAS to automate existing processes severely limits its transformative potential. Real value comes from rethinking workflows to leverage MAS for dynamic and holistic-problem solving. A strong partnership between technical and business teams is essential to challenge the status quo. Customers are transitioning from bouncing customers between departments to answer complex queries, to empowering each department to answer questions more quickly, to ultimately consolidating everything into one MAS-driven department with oversight.

A key point to remember is that even though we are reimagining our current process, this doesn’t mean we need to do everything at once. If we want to increase the number of calls routed to a virtual agent, we should first identify the initial tranche of calls to address. Then, we can incrementally expand the types or topics the virtual agent can handle to ensure customer satisfaction and maintain overall support quality.

For example, this is how we sequentially move through key steps in a Multi-Agentic System program:

Misstep 2: Underestimating collaboration design effort

A critical error is under-resourcing the design of agent collaboration-particularly in defining roles, communication protocols , and conflict resolution strategies.

As MAS evolves, it's increasingly important to know what, when, and why a specialist agent should be engaged. But how do you validate this orchestration logic? Through rigorous testing using ground truth evaluation and high-quality test data.

Customers that succeed in this area have a clear understanding of what “good” versus “bad” answers look like across different question types. These examples are critical in building agents that can determine which tools, other agents, services, verbosity, tonality, and format to use when providing a response.

Misstep 3: Delaying governance and ethical planning

Treating governance, ethics, and monitoring as afterthoughts invites significant risks, such as program delays, bias amplification, and critical policy gaps. The best way to achieve this with MAS is by embedding responsible AI principles, including establishing clear rules, audit trails and transparency. The old adage, “move slow to move fast,” becomes more relevant as we increase complexity.

For example, if bias monitoring is not considered until late in deployment, a virtual agent on an e-commerce platform might put too much weight on a customer’s zip code, displaying higher-priced products to those in wealthier areas and budget options to customers in lower-income zip codes. This could create an unfair shopping experience, where certain groups feel excluded or underserved, ultimately harming the brand’s reputation. As a result, there is rework, redesign and the need to rollback updates to go through the solution design and testing processes again, adding upwards of six months of additional work.

These concepts and the teams responsible for them must be incorporated from day 1 of a MAS project.

Get started

Based on these field insights, consider prioritizing the following:

Develop a MAS strategy: Start small, think big
Prioritize governance, ethics and trust from day one
Foster a collaborative culture that puts your user first: IT and business unite

Google is here to support. Check out how Vertex AI supports building and managing MAS. Google Cloud Consulting brings an added layer of insights and expertise, helping organizations navigate the nuances of MAS adoption and harness its transformative power. Talk to a Google Cloud sales specialist today!

Pluto AI: Revolutionizing AI accessibility and innovation at Magyar Telekom

Fri, 30 May 2025 16:00:00 +0000

In today's rapidly evolving technological landscape, artificial intelligence (AI) stands as a transformative force, reshaping industries and redefining possibilities. Recognizing AI's potential and leveraging its data landscape on Google Cloud, Magyar Telekom, Deutsche Telekom’s Hungarian operator, embarked on a journey to empower its workforce with AI knowledge and tools. This endeavor led to the creation of Pluto AI — an internal AI platform that has grown into a comprehensive framework for diverse AI solutions.

As one of Hungary's largest telecommunications operators, Magyar Telekom's ultimate vision is to embed AI into every aspect of its operations, empowering every employee to leverage AI's potential. Pluto AI is a significant step toward achieving this goal, fostering a culture of innovation and data-driven decision-making.

Magyar Telekom's leadership recognized that AI proficiency is now essential for future success. However, the company faced challenges, including employees with varying levels of AI understanding and a lack of accessible tools for experimentation and practical application. As a result, Magyar Telekom aimed to democratize AI knowledge and foster a culture of experimentation by building a scalable solution that could adapt to its evolving AI needs and support a wide range of use cases.

To enable business teams across Magyar Telekom to utilize generative AI, the Pluto AI team developed a simple tool that provided a safe and compliant way to prompt large language models (LLMs). They also created educational content and training for business teams on how to use gen AI and what opportunities it brings. This approach provided other teams with the building blocks to quickly construct the AI solutions they needed.

With Pluto AI, Magyar Telekom spearheaded the successful adoption of gen AI across the company, quickly expanding the platform to support additional use cases without the need for the central platform team to have a deep understanding of them.

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Developing Pluto AI

Magyar Telekom’s AI Team partnered with Google Cloud Consulting to accelerate the development of Pluto AI. This collaboration ensured that the platform was built on best practices, aligned with industry standards, and met security and compliance requirements of a regulated industry.

Here are some of the key features and functionality of Pluto AI:

1. Modular framework

Pluto AI's modular architecture allows teams to seamlessly integrate, change, and update various AI models, tools and various architectural patterns. This flexibility enables the platform to cater to a wide range of use cases and rapidly evolve alongside Magyar Telekom’s AI strategy.

The core modules of Pluto AI include:

Large language models: Pluto AI integrates with state-of-the-art LLMs, enabling natural language understanding, text and image generation, and conversational AI applications.
Code generation and assistance: The platform supports code generation, autocompletion, and debugging, boosting developer productivity and code quality. Pluto AI provides both a coding model, accessible via its user interface, for all development levels and IDE integration for experienced coders.
API: Pluto AI’s models can be called via API, enabling all parts of Magyar Telekom to utilize and integrate AI capabilities into their existing and new solutions.
Retrieval augmented generation (RAG) with grounding capabilities: RAG combines LLMs with internal knowledge sources, including multimodal content like images and videos. This enables teams to build AI assistants that can access and synthesize information from vast datasets and add evidence like extended citations from both corporate and public data to their responses.
Customizable AI assistants: Users can create tailored, personalized AI assistants by defining system prompts, uploading documents, and fine-tuning model behavior to meet their business needs.

2. Technical implementation

Pluto AI runs on Compute Engine using virtual machines, providing scalability, reliability, and efficient resource management. The platform also utilizes foundation models from the Model Garden on Vertex AI, including Google’s Gemini, Imagen, and Veo models, Anthropic’s Claude 3.5 Sonnet, and more. Magyar Telekom also deployed ElasticSearch on Google Cloud to store the knowledge bases necessary for enabling RAG workflows.

In addition to these core components, Pluto AI also utilizes other Google Cloud services to help develop production-ready applications, such as Cloud Logging, Pub/Sub, Cloud Storage, Firestore, and Looker.

3. User interface and experience

Pluto AI's intuitive interface makes AI tools accessible to users with varying technical expertise. A dropdown menu allows users to easily navigate between different modules and functionalities. The platform's design prioritizes user experience, ensuring that employees can leverage AI capabilities without a steep learning curve.

Impact and adoption

Pluto AI has seen impressive adoption rates, with hundreds of daily active users across different departments. The platform's user-friendly design and practical applications have garnered positive feedback from Magyar Telekom employees.

In addition, Pluto AI has enabled the development of various AI assistants, including legal and compliance assistants that accelerate contract review, identify compliance risks, and analyze legal documents. Knowledge management assistants have enhanced knowledge sharing and retrieval across the organization, while software development has benefited from code generation and assistance tools. Additionally, AI-powered chatbots that handle routine inquiries have significantly improved customer support experiences.

Magyar Telekom has seen quantifiable results since rolling out Pluto AI. These include hundreds of daily unique users, tens of thousands of API calls, an estimated 20% reduction in the time spent reviewing legal documents, and a 15% decrease in code defects.

Vision and future roadmap for Pluto AI

Magyar Telekom sees Pluto AI as a key part of its AI strategy going forward. To maximize its impact, the company intends to expand the platform to more markets, business units, and departments within the organization. Additionally, Magyar Telekom is looking into the possibility of offering Pluto AI as a service or a product to other Deutsche Telekom markets. The company is also planning to build a library of reusable AI modules and frameworks that can be easily adapted to different use cases.

Magyar Telekom is pursuing several key initiatives to enhance Pluto AI and expand its capabilities. These efforts include investigating the potential of agent-based AI systems to automate complex tasks and workflows, adding a language selector for multilingual support to cater to a diverse user base, and developing an enhanced interface for managing RAG solutions, monitoring usage, and tracking performance metrics. Magyar Telecom also plans to continue developing dashboards for monitoring and optimizing cloud resource usage and costs.

Pluto AI has transformed Magyar Telekom's AI landscape, making AI accessible, practical, and impactful. By providing a user-friendly platform, fostering experimentation, and delivering tangible business value, Pluto AI has set a new standard for internal AI adoption.

Inside Infinity Learn's AI Tutor, powered by Google Cloud

Mon, 12 May 2025 16:00:00 +0000

Late-night study sessions, complex equations, and immense pressure: for millions of students in India, the IIT JEE Main exam and NEET for medical colleges are critical gateways for their futures. Exam success hinges on intricate reasoning within physics, chemistry, mathematics, zoology, and botany. These students require a clear, step-by-step understanding of the topics, not just the correct answers.

Recognizing this vital need for deep comprehension and problem-solving, Infinity Learn, a pioneer in tech-forward education, collaborated with Google Cloud Consulting to develop an AI tutor to assist students during exam prep. The goal wasn't to merely create a question-answering tool, but a true tutor.

Acting as a custom search engine, the AI tutor allows students to learn through detailed guidance for solving math, physics, and chemistry problems. This searchable system aims to foster in-depth knowledge via Google Cloud’s Vertex AI Retrieval Augmented Generation (RAG) services. Using a cutting-edge Gemini 2.0 Flash model, the AI tutor enables students to independently find the answers they need across faculty resources, helping them truly grasp the how and the why behind solutions.

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Powered by Agent Builder and Vertex AI RAG Engine

The biggest challenge early on in development was building a system that could do more than just anticipate exam questions. It needed to:

Solve multi-step problems logically, mirroring a human tutor's approach.
Articulate each step in a way that promotes conceptual clarity.
Serve a large user base (both free and paid) while balancing cost and accuracy.
Analyze and self-correct errors to continuously refine performance.

In a collaborative effort, Google Cloud Consulting and Infinity Learn used Agent Builder for experimentation and RAG Engine on Vertex AI for refinement.

They began with Vertex AI's Agent Builder, an out-of-the-box RAG system that allowed for rapid prototyping and quick iteration. Using Vertex AI Search within Agent Builder, they were able to supply the AI language models with Infinity Learn's collective library of resources, ensuring answers were relevant, accurate, and grounded.

For further customization, they then implemented a do-it-yourself RAG engine with Vertex AI Search as retriever. This offered more flexibility when selecting the retrieval mechanisms and generation model, as well as granular control for fine-tuning the AI tutor’s accuracy and reasoning capabilities while using Google's search infrastructure.

Technical architecture: A blended architecture which leverages state–of-the-art Vertex AI Search as the backend engine and Gemini 2.0 Flash and thinking-mode as the generative model along with a custom evaluation method.

Beyond accuracy: Measuring real-world effectiveness

Following implementation, the team launched a thorough evaluation for the AI tutor. This model-based custom evaluation leveraged Vertex AI workbench and subject matter experts to confirm AI accuracy within a variation of up to 3-5% (as compared to human reviewer-based accuracy).

The evaluation process focused on measuring and analyzing errors related to conceptual, computational, and procedural mistakes. Mitigating these errors can help in the self-correction of generated answers, providing smarter future responses for the solution, valuable data for ongoing refinement, and insight into the AI's ability to pinpoint areas where students commonly struggle.

The AI tutor's impact and a shift in learning

Through combining advanced reasoning, custom AI, and flexible data retrieval, the AI tutor yielded a remarkable 90% accuracy when addressing student queries across an average of 500 exam preparation questions. Essentially, this level of precision means students can confidently rely on the AI tutor for the majority of their questions.

But more than just accuracy, this technology is driving a fundamental shift in education. Students are now able to move beyond rote memorization and learn at their own pace through the AI tutor’s detailed, comprehensive responses.

As an example, imagine a student struggling with a difficult mathematics problem late at night. Instead of getting stuck for hours, they can ask the AI tutor, giving them not only the correct answer, but a foundational understanding of the basic principles and the steps to get there.

This personalized, always-on exam companion continues to encourage independent thinking and understanding, thanks to a tech-first approach to education, and Infinity Learn’s collaborative work with Google Cloud Consulting.

"Since implementing the AI tutor, we've seen a remarkable shift in how students approach complex problems. They're engaging with the underlying concepts on a deeper level. Google Cloud Consulting's expertise was instrumental in helping us build a truly innovative AI tutor that goes beyond surface-level answers and cultivates real understanding." – Amit Bansal, Chief Product Officer, Infinity Learn

Inspired by Infinity Learn's transformative approach?

The partnership between Google Cloud and Infinity Learn by Sri Chaitanya is just one of the latest examples of how we’re providing AI-powered solutions to solve complex problems and help organizations drive desired outcomes.

Discover how Google Cloud Consulting can help you leverage Vertex AI to transform your own challenges or business needs. The Google Cloud Consulting portfolio provides unified services, bringing together offerings throughout multiple specializations like technical account management, professional services, customer success, and more. Check out Google Cloud Consulting’s full portfolio of offerings, or learn how to get started with Vertex AI.

New services to help enterprises adopt AI from Google Cloud Consulting

Wed, 09 Apr 2025 12:00:00 +0000

Enterprise customers are coming to Google Cloud to transform their businesses with AI, and many are turning to our seasoned experts at Google Cloud Consulting to help implement these innovations.

Working alongside our many partners, Google Cloud Consulting teams are helping customers identify the right use cases for AI, deliver them safely and securely, and then generate strong ROI. In fact, engagements focused on implementing Google Cloud AI have become the fastest-growing area within our consulting practice over the past year — indicating the tremendous level of excitement around Google’s AI offerings.

This week at Google Cloud Next, we’re leaning into this success with the launch of several new offers, delivered by Google Cloud Consulting and our partners. These are all aimed at a simple, yet important goal: making it even easier for enterprises to capitalize on our entire portfolio of AI models, AI-optimized infrastructure, AI platforms, and agentic technologies.

We’re also expanding access to Delivery Navigator, a platform that offers a wide range of resources for teams working on cloud-based projects, including project plan templates, technical instructions, predictive insights, and smart recommendations.

And finally, to demonstrate the proven business impact of our work, we’re excited to showcase a pair of significant projects our team has completed with industry leaders: Airbus and Broadcom.

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Accelerate Your Transformation: New Service Offerings

To accelerate our customers' digital transformations and streamline technology adoption, we're introducing three new pre-packaged service offerings. These tailored offers provide a clear and proven path that can lead enterprises from concept to deployment, enabling them to rapidly adopt our technologies and avoid costly missteps. Each offering can be customized to meet your unique needs, ensuring an efficient and effective technology implementation journey.

New offers available this week:

Agentspace Accelerator provides a structured approach to connecting and deploying AI-powered search within organizations, so employees can easily gain access to relevant internal information and resources when they need it. As part of this service offer, our team of experts facilitate the secure integration of enterprise data with Gemini's advanced reasoning and Google-quality search, along with customizable add-ons for cloud setup, data preparation, and strategic enablement. The Agentspace Accelerator offering is the stepping stone for organizations to create their own AI-powered agents, agentic workflows, and more within a single unified platform.
Optimize with TPUs helps customers migrate workloads to our purpose-built AI chips, TPUs, so they can maximize the spend and performance of their AI inference and training. TPUs are designed to scale cost-efficiently across a variety of use cases, including chatbots, code generation, media content creation, synthetic speech, personalization models, and recommendation engines. Through a six-week engagement, Google Cloud experts will develop a custom backend wrapper around your existing AI Infrastructure, enabling you to easily shift workloads to TPUs while maintaining the flexibility to toggle to other chips as needed.
Oracle on Google Cloud empowers customers to fully leverage the benefits of our partnership with Oracle, combining Oracle databases and applications with Google Cloud's advanced platform and AI capabilities for enhanced database and network performance. Through a tailored engagement, Google Cloud experts will assist in deploying and optimizing Oracle through a variety of modalities, such as bring-your-own-licenses with Google Kubernetes Engine or Oracle Cloud Infrastructure with Google Cross-Cloud Interconnect. This offering enables customers to improve database and network speed, while gaining streamlined access to Google Cloud’s AI and data tools to help simplify the creation of AI-powered applications.

Next up: The AI-enhanced Delivery Navigator platform

To further empower our customers’ cloud journeys, we’re excited to expand access to Delivery Navigator, a platform designed to bring efficiency and confidence to Google Cloud projects. Currently, our partners and consulting teams use Delivery Navigator to access proven delivery methodologies and best practices that help them guide migrations and technology implementations efficiently and safely.

Starting in October, we will begin to roll out Delivery Navigator to customers as well, available in preview. This means participating customers will have direct access to the same frameworks, tooling, tips, and best practices used by Google Cloud’s own teams.

Built around a conversational AI-chat interface, Delivery Navigator offers a wide range of resources for teams working on cloud-based projects, including project plan templates, detailed technical instructions with example code, and predictive insights and smart recommendations to help teams proactively address technical challenges. It covers many of the common variables encountered during solution implementation, as well as rarer hurdles, and there’s plenty of guidance on how to optimize Google Cloud deployments and accelerate time-to-value.

By providing access to the same advanced frameworks and AI enhancements used by our own experts, Delivery Navigator enables a smoother, faster, and more successful path to realizing the full value of Google Cloud.

Bringing Cloud and AI Projects to Life

Customers are already achieving remarkable outcomes with the support of Google Cloud Consulting experts and our partner ecosystem, and this week, we’re excited to share our work with enterprise customers like Airbus and Broadcom. While each customer’s needs differ, the common theme is that Google Cloud Consulting is helping enterprises implement Google Cloud technology and migrate workloads within complex IT environments – and doing so safely and securely.

Airbus’ IT landscape has been streamlined in Canada through a strategic migration, within a post-merger integration context, from on-prem servers to Google Cloud. This transformation provides Airbus with enhanced visibility and ownership of its complex IT infrastructure in Canada, enabling the company to modernize their systems with the migration of more than 500 systems.

Leveraging robust infrastructure and migration tools, a rapid and seamless migration of 2.5 petabytes of data was achieved by Airbus, with on-time completion. This revamped tech stack helps Airbus to support the increase of production rates in Canada and contributes to strengthening airline trust.

Broadcom is driving its digital transformation by optimizing its compute and data landscape through a strategic VMware migration to Google Cloud — and boosting employee efficiency with the deployment of Gemini Code Assist.

Broadcom engaged a group of experts from Google Cloud Consulting to efficiently migrate twelve SaaS products, including their data infrastructures, while maintaining zero downtime. This unified data environment empowers Broadcom to access and analyze data with greater efficiency, leading to deeper product insights and enhanced customer experiences.

Furthermore, Broadcom is rolling out Gemini Code Assist to their employees, through a robust enablement program led by Google Cloud Consulting that features hands-on training, accessible office hours, and ongoing chat support.

Building the future together

At Google Cloud Consulting, we're passionate about empowering businesses to thrive in the cloud and AI era. We're committed to guiding customers every step of the way, from initial planning and implementation to ongoing optimization. Contact us today to discover how we can help you achieve your business objectives in this new era of technology.

Optimizing image generation pipelines on Google Cloud: A practical guide

Fri, 21 Feb 2025 17:00:00 +0000

Generative AI diffusion models such as Stable Diffusion and Flux produce stunning visuals, empowering creators across various verticals with impressive image generation capabilities. However, generating high-quality images through sophisticated pipelines can be computationally demanding, even with powerful hardware like GPUs and TPUs, impacting both costs and time-to-result.

The key challenge lies in optimizing the entire pipeline to minimize cost and latency without compromising on image quality. This delicate balance is crucial for unlocking the full potential of image generation in real-world applications. For example, before reducing the model size to cut image generation costs, prioritize optimizing the underlying infrastructure and software to ensure peak model performance.

At Google Cloud Consulting, we've been assisting customers in navigating these complexities. We understand the importance of optimized image generation pipelines, and in this post, we'll share three proven strategies to help you achieve both efficiency and cost-effectiveness, to deliver exceptional user experiences.

A comprehensive approach to optimization

We recommend having a comprehensive optimization strategy that addresses all aspects of the pipeline, from hardware to code to overall architecture. One way that we address this at Google Cloud is with AI Hypercomputer, a composable supercomputing architecture that brings together hardware like TPUs & GPUs, along with software and frameworks like Pytorch. Here's a breakdown of the key areas we focus on:

1. Hardware optimization: Maximizing resource utilization

Image generation pipelines often require GPUs or TPUs for deployment, and optimizing hardware utilization can significantly reduce costs. Since GPUs cannot be allocated fractionally, underutilization is common, especially when scaling workloads, leading to inefficiency and increased cost of operation. To address this, Google Kubernetes Engine (GKE) offers several GPU sharing strategies to improve resource efficiency. Additionally, A3 High VMs with NVIDIA H100 80GB GPUs come in smaller sizes, helping you scale efficiently and control costs.

Some key GPU sharing strategies in GKE include:

Multi-instance GPUs: In this strategy, GKE divides a single GPU in up to 7 slices, providing hardware isolation between the workloads. Each GPU slice has its own resources (compute, memory, and bandwidth) and can be independently assigned to a single container. You can leverage this strategy for inference workloads where resiliency and predictable performance is required. Please review the documented limitations of this approach before implementing and note that currently supported GPU types for multi-instance GPUs on GKE are NVIDIA A100 GPUs (40GB and 80GB), and NVIDIA H100 GPUs (80GB).
GPU time-sharing: GPU time-sharing lets multiple containers access full GPU capacity using rapid context switching between processes; this is made possible by instruction-level preemption in NVIDIA GPUs. This approach is more suitable for bursty and interactive workloads, or for testing and prototyping purposes where full isolation is not required. With GPU time-sharing, you can optimize GPU cost and utilization by reducing GPU idling time. However, context switching may introduce some latency overhead for individual workloads.
NVIDIA Multi-Process Service (MPS): NVIDIA MPS is a version of the CUDA API that lets multiple processes/containers run at the same time on the same physical GPU without interference. In this approach, you can run multiple small-to-medium-scale batch-processing workloads on a single GPU and maximize the throughput and hardware utilization. While implementing MPS, you must ensure that workloads using MPS can tolerate memory protection and error containment limitations.

Example illustration of GPU sharing strategies

2. Inference code optimization: Fine-tuning for efficacy

When you have an existing pipeline written natively in PyTorch, you have several options to optimize and reduce the pipeline execution time.

One way is to use PyTorch's compile method, which enables Just-in-time (JIT) compiling PyTorch code into optimized kernels for faster execution, especially for the forward pass of the decoder step. You can do this through various backend computers such as NVIDIA TensorRT, OpenVINO or IPEX depending on the underlying hardware. You can also use certain compiler backends at training time. A similar JIT compilation capability is also available for other frameworks such as JAX.

Another way to improve code latency is to enable Flash Attention. By enabling the torch.backends.cuda.enable_flash_sdp attribute, PyTorch code natively runs Flash Attention where it is helpful in speeding up a given computation, while automatically selecting another attention mechanism if Flash isn’t optimal based on the inputs.

Additionally, to reduce latency, you also need to minimize data transfers between the GPU and CPU. Operations such as tensor loading, or comparing tensors and Python floats, incur significant overhead due to the data movement. Each time a tensor is compared with a floating-point value, it must be transferred to the CPU, incurring latency. Ideally, you should only load and offload a tensor on and off the GPU once in the entire image generation pipeline; this is especially important for image generation pipelines that utilize several models, where latency cascades with each model that is run. Tools such as PyTorch Profiler help us observe the time and memory utilization of a model.

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3. Inference pipeline optimization: Streamlining the workflow

While optimizing code can help speed up individual modules within a pipeline, you need to take a look at the big picture. Many multi-step image-generation pipelines cascade multiple models (e.g., samplers, decoders, and image and/or text embedding models) one after the other to generate the final image, often on a single container that has a single GPU attached.

For Diffusion-based pipelines, models such as decoders can have significantly higher computational complexity and hence take longer to execute, especially as compared with embedding models, which are generally faster. That means that certain models can cause a bottleneck in the generation pipeline. To optimize GPU utilization and mitigate this bottleneck, you may consider employing a multi-threaded queue-based approach for efficient task scheduling and execution. This approach enables parallel execution of different pipeline stages on the same GPU, allowing for concurrent processing of several requests. Efficiently distributing tasks among worker threads minimizes GPU idle time and maximizes resource utilization, ultimately leading to higher throughput.

Furthermore, by maintaining tensors on the same GPU throughout the process, you can reduce the overhead of CPU-to-GPU (and vice-versa) data transfers, further enhancing efficiency and reducing costs.

Processing time comparison between a stacked pipeline and a multithreaded pipeline for 2 concurrent requests

Final thoughts

Optimizing image-generation pipelines is a multifaceted process, but the rewards are significant. By adopting a comprehensive approach that includes hardware optimization for maximized resource utilization, code optimization for faster execution, and pipeline optimization for increased throughput, you can achieve substantial performance gains, reduce costs, and deliver exceptional user experiences. In our work with customers, we've consistently seen that implementing these optimization strategies can lead to significant cost savings without compromising image quality.

Ready to get started?

At Google Cloud Consulting, we're dedicated to helping our customers build and deploy high-performing AI solutions. If you're looking to optimize your image generation pipelines, connect with Google Cloud Consulting today, and we’ll work to help you unlock the full potential of your AI initiatives.

^{We extend our sincere gratitude to Akhil Sakarwal, Ashish Tendulkar, Abhijat Gupta, and Suraj Kanojia for their invaluable support and guidance throughout the crucial experimentation phase.}

Cloud migrations made easy: a guide to migrating from AWS to Google Cloud

Mon, 27 Jan 2025 17:00:00 +0000

More and more customers deploy their workloads on Google Cloud. But what if your workloads are sitting in another cloud? Planning, designing, and implementing a migration of your workloads, data, and processes is not an easy task. It gets even harder if you have to meet requirements that have an impact on the migration, such as avoiding downtime (also known as a zero-downtime migration). Moreover, some migrations require a certain amount of refactoring, for example, adapting your workloads to a new environment. This opens up a series of challenges, especially if you’re dealing with third-party or legacy software. You might also need to adapt your deployment and operational processes to work with your new environment.

And what if you don’t want to migrate all your workloads? Even if you’re not moving everything to Google Cloud, adopting a multicloud approach is still a migration. Many organizations choose to keep some workloads in their current cloud provider while moving others to Google Cloud.

Although managing workloads across multiple clouds has its own challenges, particularly when it comes to workload distribution and inter-cloud connectivity, a well-executed multicloud strategy lets you maintain flexibility, avoid vendor lock-in, and improve system resilience.

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To help you in your migration journey, we published a series of reference guides about migrating from Amazon Web Services (AWS) to Google Cloud. This series aims to help you design, plan, and implement a migration process from AWS to Google Cloud. It can also help decision makers who are evaluating migration opportunities and want to explore what it looks like to migrate. For example, the series includes guides that cover migration journeys, such as:

From AWS compute services to Google Cloud

From AWS data storage services to Google Cloud

Migrate from Amazon S3 to Cloud Storage

From AWS database services to Google Cloud

From AWS data analytics services to Google Cloud

Migrate from Amazon RedShift to BigQuery

From AWS integration services to Google Cloud

Migrate from AWS Step Functions to Workflows

These guides follow the phases of the Google Cloud migration framework (assess, plan, migrate, optimize) in the context of specific AWS to Google Cloud migration use cases.

This approach helps to avoid big-bang, risky migrations, when working on each migration plan task. For details about completing each task of this migration plan, see the AWS to Google Cloud migration guides.

What’s next

Learn how to get started with your migration from AWS to Google Cloud today!
In case you need any help, Google Cloud offers several resources to help you migrate your workloads, data, and processes at scale.
Need a more visual guide? Download our new migration guide & checklist.

Ready to get started?

Ready to learn more? Learn more about migrating to Google Cloud and discover how Google Cloud Consulting can help you learn, build, operate and succeed.

^{Acknowledgements: Alex Cârciu, Cloud Solutions Architect, Xiang Shen, Cloud Solutions Architect}

Virtual Try On for Saree

Tue, 30 Jul 2024 17:00:00 +0000

Meesho, a leading Indian e-commerce platform is an online marketplace that facilitates trade between suppliers and customers. One of the main challenges that their end suppliers face in the cataloging process is the cost and time required for capturing models wearing different apparel products. So Meesho's team partnered with Google Cloud Consulting (GCC) to build the Virtual Try On solution for the most complex garment, saree! For a given blouse image, saree body image and pallu image provided by the end supplier, 2D images and 3D models are generated.

Let's take a deeper look at the different components of the tech stack used to build the 2D and 3D-based solution.

The architecture represents the end-to-end pipeline for their Virtual Try On service utilizing the Google Cloud Platform. The pipeline aims to facilitate the process of uploading photos of sarees by end distributors, which will subsequently be processed by a custom AI solution. The processed image will be passed to Vertex AI Imagen, which will update the background and upscale the image to enhance its quality. The resulting image can be used to generate catalogs for the products. The architecture includes the following pipeline stages:

1. Requirements

Below are few image guidelines that need to be adhered:

To build the solution, static images of models wearing a plain white sari without any patterns or reflections are required . It’s advised to have a diversity in the model poses - front pose, back pose, side pose etc.
Apart from the models required, the saree images uploaded by the end suppliers should be without wrinkles and the pics should be taken in proper lighting conditions. The borders of the saree (if present) should be correctly captured.

2. Saree Reconstruction

The idea is to convert the input images into a standardized format, so that it can be consumed in the downstream sari draping components. We used an overlay and blending method where we first overlay the two input images by a fixed; percentage and then use a python library called “img2texture” to blend the overlapped portion. This overlay and blending method results in a seamless intersection point and doesn’t show any visible inconsistencies at the intersection. This method is also good when we plan to blend two very different patterns (as in our case, the pallu and sari body image can be completely different patterns). The overlay and blending of completely different images also looks seamless and very realistic. The following image shows the inputs and outputs of sari reconstruction module:

3. 2D Approach

A. Labeling

We used Label Studio to label the different segments in the saree. Below are the 4 custom segments that were identified in a front facing model:

Pallu: Pallu section of the saree
Top Half: Upper portion of the saree, the region that wraps around the upper body.
Bottom Half: Lower section of the saree, capturing the area that drapes over the lower body.
Blouse: Designated for the blouse section

B. TPS Warping

TPS warping, which stands for Thin-Plate Spline warping, is a technique used to manipulate and deform images or other data points. It is a spline-based technique for data interpolation and smoothing. Label studio has been used to annotate the warping points. Below are the results of this step:

C. Pleat Enhancement

The idea is to make the bottom pleats look realistic by hiding some part of patterns between two pleats. For this, we firstly label the entire bottom portion of the saree as pleat masks (png masks). Next, we put some space between ordered pleats so that we can hide some portion of the pattern image between consecutive pleats. Then, we crop the pattern accordingly which would eventually give an effect that few portions of the sari are hidden between the pleats. And finally, we apply the light mask on the image to get a realistic output. The following image shows the final output from this step:

As we can see in the image above, some portions of the pattern are hidden between pleats and it is making the overall draping look very realistic.

D. Light Masking

Masking is the process through which the pattern image is overlaid on the existing model. In the below image, for a given bottom half segment and a saree pattern, the masking function modifies the model image by multiplying each pixel of the pattern with the base mask to generate the 2D image.

E. Compose image service will merge all the segments into the result image.

All the segments (Blouse, Top Half, Bottom Half and Pallu sections) are merged together to form the final image.

4. 3D Approach

A. 3D Mesh Rendering

For the 3D approach, we need the 3D files of the model draping the saree. These files come in various formats, however for our experimentation we went ahead and used the OBJ file.

The 3D experiments are performed on Blender software, which is an easy to use 3D modeling and rendering software.The 3D pipeline primarily uses the .blend file as the source of truth and then manipulates the elements within this file to create 3D renders for each of the saree patterns.

B. 3D Mesh Scene Photography Service

In order to click realistic and showcase worthy pictures, it is necessary to determine the right camera positions and angles(orientation). We do this manually once, and take a note of all the camera positions that we deem fit, next we create a camera_profiles json that specifies the location and orientation of each of these camera positions whilst the key is set to the name of that particular configuration.

5. GenAI Magic

A. Background Enhancement

Vertex AI Imagen is used to edit the background of the image generated. Mask-based editing lets you specify a targeted area to apply edits to.

B. Resolution Upscaling

Vertex AI Imagen lets you increase the size of the generated, or edited images without losing quality.

“Draping the product on a model has a huge impact on our users' ability to assess the product, perceive quality, color better and imagine how this would look on them. For our sellers, being able to automate this gives a productivity boost and a jump in likelihood of users buying the product. With this collaboration with Google Cloud, we've been able to marry the best of Google and Meesho tech to create a much better shopping experience.

This experience is coming to a Meesho app near you soon!” according to Divyesh Shah, Vice President - Engineering at Meesho.

Fast track end-to-end deployment with Google Cloud Consulting (GCC)

The partnership between Google Cloud and Meesho is just one of the latest examples of how we’re providing AI-powered solutions to solve complex problems to help organizations drive the desired outcomes. Meesho entrusted GCC to collaborate with their teams to build the state of the workflows for their business requirements. The GCC portfolio provides a unified services capability, bringing together offerings across multiple specializations, into a single place. This includes services from learning to technical account management to professional services and customer success.

Learn more about how Google Cloud Consulting can help you learn, build, operate and succeed.

Cloud FinOps and GKE: How Generali optimized spend with Google Cloud Consulting

Fri, 31 May 2024 16:00:00 +0000

“As a small platform team, how do we optimize hundreds of workloads running in multiple GKE clusters to be cost-effective, without sacrificing performance or reliability?”

This was the question facing the team in charge of a suite of applications hosted on Google Kubernetes Engine (GKE) at Generali Switzerland in the summer and Autumn of 2023 — a tough challenge that would resonate with many of our customers.

GKE can be difficult to optimize for a couple of reasons:

There isn’t one single metric which you can use to optimize a cluster reliably. Instead, you’ll need to take into account multiple factors to determine what an effective optimization might be (keep reading to learn more about the four golden signals of GKE).
GKE is often decentralized to some extent. A central team maintains the clusters, and application teams deploy the workloads on those clusters. This model works well as it separates the application and the infrastructure into different spheres of responsibility. However, it also means that if the application teams oversize their requests, it will waste resources. Engineers need to be conscious of sizing workloads correctly.

With GKE usage contributing to a significant portion of their cloud spend, Generali felt there was room for improvement. Generali engaged Google Cloud Consulting to help the global insurance company explore how best to optimize its clusters and workloads, across both its production and non-production environments. The result was an identification of annual savings of more than €260,000 per year, representing a 41% reduction in GKE spend, achieved over a 12-week project between July and September 2023.

Analyze the ‘golden signals’

So how was this achieved? What tools were used? And, more importantly, how can you achieve the same results?

When optimizing GKE spend, we always start by analyzing what we call the “golden signals,” a set of metrics that you can use to analyze the efficiency of your GKE cluster. These signals include the following:

Workload rightsizing and utilization
Demand-based autoscaling
Bin packing
Discount coverage

You can read more about all of these signals in the State of Kubernetes Cost Optimization report, but think of them as levers you can pull to optimize the costs of your cluster. So how did Generali use these signals?

Workload rightsizing

Let's start with workload rightsizing. Workload rightsizing is about setting requests on CPU and memory as close as possible to actual resource utilization. It should be the very first step when optimizing the cluster because workload rightsizing changes affect the optimal settings for bin packing and discounts. As an activity, workload rightsizing is relatively simple when you have a small number of applications to consider. However, it becomes extremely difficult to enforce at scale when you have many teams and hundreds of applications running on your clusters like Generali.

Working with Google Cloud Consulting, Generali observed that while most applications had requests set, resource utilization was less than 20% of what was being requested — a huge opportunity for saving. To optimize this golden signal, Generali reviewed and lowered all application requests and conducted sessions with its application teams to help educate and upskill on the importance of setting correct requests. This amounted to approximately €100,000 of savings per year!

Tip: You can check how your workloads are rightsized by using the out-of-the-box GKE Cost Optimization graphs, along with the recommended Requests.The GKE Workloads at Risk dashboards, available in the Google Cloud console, also point out where containers are not setting any requests, as well as a wealth of other information about your clusters.

Rightsizing your requests can have a dramatic impact on cluster efficiency and cost. In addition, poorly set requests, such as not setting any requests or setting them too low, can also affect your application reliability as workloads might be evicted from the cluster. You should always investigate this signal first and make sure you continually monitor those requests.

Demand-based autoscaling

The next signal that Generali considered was demand-based autoscaling, which means configuring the application and the infrastructure to autoscale based on its end user demand. This can cut significant costs for workloads that have fluctuating traffic. For example, Generali already had infrastructure autoscaling in place but discovered after analyzing this signal that it didn’t always trigger properly and, in particular, would not scale down when traffic was low. This was caused by the way the pods had been configured to autoscale and some application configuration errors.

To change this behavior, Generali fixed the issues preventing the scale down and configured the horizontal and vertical pod autoscaler based on best practices provided by Google Cloud Consulting.

For Generali, unblocking Vertical Pod autoscaling and Horizontal Pod autoscaling for certain workloads helped realize immediate savings as the company was no longer paying for nodes they didn’t need. They also applied a scale to zero technique, which realized an estimated €35,000 annually in savings.

Bin packing

The third signal, bin packing, involves picking the best number and characteristics of the nodes to fit the workload. The better you pack apps onto nodes, the more you save. Generali was using GKE Standard and various node pools, including Spot VMs for better bin packing. While the percentage of bin packing was already over 60%, there was still room to improve. The team focused on migrating from N2 machines to cost-optimized E2 machines to provide a better balance of performance to cost. With this change and further bin packing optimizations, Generali identified around €105,000 of yearly savings .

To optimize even more, Generali also experimented with GKE Autopilot, which has a lower total cost of ownership because you aren't billed for unused capacity on your nodes. GKE picks and manages the most optimal nodes for you, leading to better bin packing, and facilitates many operational maintenance tasks, such as autoscaling settings, which you would otherwise have to do yourself. To learn more, read the Autopilot documentation guide or check out how GKE Standard and GKE Autopilot features compare.

Discount coverage

Finally, discount coverage is a crucial part in getting the best ROI on GKE. Specifically, it's important to ensure that any cloud resources that can be covered with a committed use discount — or CUD — is covered.

CUDs are a way to get significant discounts on your GKE usage in exchange for committing to use those resources for a set period (typically 1 or 3 years). They are most effective when you have a predictable and consistent need for GKE resources.

GKE offers two types of CUDs:

GKE Autopilot Mode CUDs: These offer spend-based discounts specific to GKE Autopilot clusters.
Resource-Based CUDs: These provide discounts on vCPUs and memory for GKE Standard clusters.

With CUDs, it's best to start conservatively and build coverage. Generali already had CUDs in place and opted to pause its purchasing of new commitments until after this optimization work was complete since any optimization would impact the overall amount of infrastructure in use. Generali have forecasted around €75,000 of savings using CUDs.

Great results for Generali and Google Cloud

Working with Google Cloud Consulting, Generali was able to achieve double digit percent savings on its GKE investment. According to Mohamed Talhaoui, the project lead, “If there was one takeaway from this engagement that Generali took, it was the importance of proactive financial management and optimization.“

As next steps for cost optimization, Generali plans to finalize its rightsizing and bin packing for all clusters and complete its evaluation of GKE Autopilot. The team is also preparing new commitments after all cost optimization exercises.

”The engagement with the Google Cloud Consulting FinOps team proved immensely beneficial for our organization,” Talhoui said. “Through their expertise, guidance, and focused efforts, we were able to achieve significant cost optimization within a remarkably brief timeframe, without compromising performance or quality."

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Ready to learn more? Check out our services focused on FinOps and discover how Google Cloud Consulting can help you learn, build, operate and succeed.

^{Special thanks ands huge congratulations to the GKE team at Generali Switzerland, who worked on identifying and implementing these savings and sharing their insights for this blog post: Nada Kammoun-Ksibi, Mohamed Talhaoui, Christophe Mugnier, Mohamed Marzouk, Daniele Altomare, Jomon Joseph and Gustavo Benitez.}

Unlock the business impact of gen AI with new Generative AI Ops services

Thu, 23 May 2024 13:00:00 +0000

Google Cloud customers are increasingly moving their generative AI workloads from proof-of-concept into production, and are now seeing real-world business impact from their AI investments. Many of these customers have worked with Google Cloud Consulting to apply AI in important and helpful ways. For example, Bristol Myers Squibb developed a new AI-powered interface to help its clinical study teams more easily find important information and generate documents, and Palo Alto Networks launched several new AI tools that utilize Gemini to streamline and enhance user experience in its copilots, improving productivity of security practitioners.

Moving these workloads into production requires a deep understanding of generative AI systems design, large language model architectures, prompt engineering, evaluation, and much more. Now, we’re bringing Google’s expertise in these areas to our customers at-scale, with the launch of a new service offering: Generative AI Ops. This new offering, delivered by either Google Cloud Consulting or via our comprehensive partner ecosystem, will help organizations mature their gen AI prototypes into production-grade solutions and provide support in important areas like security, model tuning and feedback, and optimization.

With the launch of Generative AI Ops, Google Cloud now offers customers both an open and optimized technology stack for building AI, and a comprehensive set of services to support customers at every stage of their AI transformations — from exploration to production.

The new Generative AI Ops services offering moves customers through the steps required to make AI applications production-ready. These include:

Prompt engineering, design, and optimization: Designing well-optimized prompts is important to ensure models can provide high-quality outputs and build user trust. Using best practices for prompt engineering, and techniques such as ReAct, retrieval augmented generation (RAG), and chain of thought, Google Cloud Consulting can help customers build solutions to improve the performance of their gen AI applications and the outputs of models. Importantly, different models are often suited to different use cases, and each of these models may require different prompting structures. Our expert teams will also help customers apply the right model to the right use case, and to apply the right prompting technique to the right model.
Performance and system evaluation: Successfully putting AI into production requires constant evaluation and feedback to improve the performance of models and applications. This services offering helps customers design and deploy an evaluation framework tailored to their applications, and build mechanisms for automated evaluation metrics using tools like AutoSxS and GenAI Eval, human evaluation, as well as hybrid approaches.
Model optimization and continuous tuning: Once a framework for performance and system evaluation is in place, gen AI applications and models still require continuous tuning and optimization. Gen AI Ops provides solutions and managed services for optimizing and tuning models based on human feedback and benchmarking. This includes improving system architecture and model selection, reducing latency and costs, and incorporating the latest APIs and and available tools to orchestrate and build AI agents using LangChain or DIY orchestrators to ensure applications run optimally.
Monitoring and observability: Having a robust monitoring solution in place is critical to ensuring AI applications are production-ready. Google Cloud Consulting can help customers build observability solutions to constantly monitor the operations and performance of their gen AI applications on a wide variety of factors, like model accuracy and hallucination, latency, throughput, hardware utilization, model drift, traffic, and costs.
Business integration and testing: It is critical that a customers’ applications and models perform well in real-world scenarios and integrate well with their business processes. Google Cloud Consulting can help customers through the careful planning required to achieve this, including setting up a scalable and secure environment on Google Cloud, designing APIs to efficiently manage interactions with various models, and implementing rigorous unit, integration, and load testing to evaluate their models’ performance under various conditions.

Train and enable customer teams

On top of the business planning and technical steps required to bring AI applications into production, training and team enablement are also critical priorities for customers wanting to see success in their cloud deployments. Through the Google Cloud Skills Boost Platform, Google Cloud offers a broad range of trainings, hands-on labs, bootcamps, and coursework to help upskill teams on generative AI, to ensure that customer teams can build, deploy, use, and manage new AI applications.

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Seamless SAP Migration to Google Cloud: Accelerating Solenis's Digital Transformation Journey

Wed, 15 May 2024 16:00:00 +0000

Editor’s note: In this post, we’ll hear from specialty chemical manufacturer Solenis about how its migration journey to SAP on Google Cloud has supercharged its innovation and unlocked new value. Headquartered in Wilmington, Delaware, the company has 69 manufacturing facilities strategically located around the globe and employs a team of over 16,000 professionals in 130 countries across six continents

Migrating SAP to the cloud isn't just a trend; it's a strategic move transforming how businesses operate. Imagine cutting costs, boosting performance, and easily scaling your resources — that's the power of SAP in the cloud.

Consider the experience of Solenis, a leading global producer of specialty chemicals for water-intensive industries, which migrated its business to a modern digital platform in the public cloud to help improve its agility, scalability, and cost-efficiency. Owned by Platinum Equity, Solenis provides a broad array of sustainable solutions, including water treatment chemistries, process aids, functional additives, cleaners and disinfectants, as well as state-of-the-art monitoring and control systems. Customers use these technologies to improve operational efficiencies, enhance product quality, protect plant assets, minimize environmental impact, and create cleaner and safer environments.

In recent years, Solenis has grown by acquisition, leading to a complex and unwieldy IT environment. To address these challenges, Solenis decided to migrate its on-premises SAP workloads, which run its mission-critical business processes, to SAP on Google Cloud. Given its rapidly increasing memory requirements, Solenis opted to forego a hardware refresh and instead chose to leverage flexible cloud-based infrastructure with High Availability (HA) capabilities to support near-zero downtime.

Google Cloud Consulting worked with Solenis to prepare its migration teams, fill the skills gap, and successfully run SAP in Google Cloud. Together, Google Cloud Consulting and Solenis migrated the company’s SAP workloads, including SAP ERP, Global Trade Services, Adobe Document Service, GRC (governance, risk, and compliance), and many other bolt-on applications — all in less than five months.

Migrating SAP to Google Cloud

With guidance from Google Cloud Consulting, Solenis was able to minimize downtime during the migration by carefully planning and testing the process, including the performance. This helped to identify and resolve any potential issues before they could cause any disruptions to Solenis’ business operations.

During the migration, for instance, the Solenis team faced a number of performance and technical issues while working to migrate workloads from its SAP ERP Central Component (ECC) and SAP Global Trade Services applications to Google Cloud. During Q&A testing, Solenis discovered an issue with one of its on-premises components that would have caused significant project delays if not addressed promptly. Working with Google Cloud Consulting, the team was able to quickly pinpoint the root cause and implement a solution before it could impact the migration process.

Choosing the right approach to moving data from on-premises to Google Cloud was another critical step to ensure the project was completed with minimal downtime and optimal success. Working with Google Cloud Consulting, Solenis decided to implement a three-step approach that yielded a significant reduction in downtime.

Data was migrated from an on-premises server to Google Cloud using a Network File Share and rsync (a remote and local file synchronization tool). This approach reduced downtime by five to six hours.

The company also took advantage of Google Cloud's managed backup and disaster recovery services to ensure that its data was always protected throughout the migration.

“We are grateful to the Google Cloud team for their exceptional support and expertise during the migration of our SAP environment from on-premises to the cloud,” said Adrian Giboi, vice president of infrastructure and operations at Solenis. “This significant accomplishment was achieved with minimal interruption to our operations, which is a testament to the dedication and proficiency of the Google Cloud team.”

Accelerating innovation and unlocking new business value

The migration to Google Cloud has delivered a number of benefits to Solenis. For example, Solenis was able to transition from Oracle databases to SAP HANA, providing a solid foundation for future performance improvements. With real-time access to data, SAP HANA can help businesses make faster and more informed decisions, deliver a more personalized and responsive customer experience, and provide a platform for innovation.

In addition, the migration helped Solenis reduce the complexity and management overhead of its previous IT environment. Google Cloud also made it easier for Solenis to perform ongoing maintenance without business interruptions. With on-premises environments, planned maintenance activities often require downtime, which can disrupt or delay service, impact productivity, or even result in potential loss in business.

With Google Cloud, such instances can be significantly reduced. Features like live migration allow for seamless transfers of virtual machines between hosts without causing downtime for customers. Additionally, Google Cloud's 99.99% compute SLA , with instances in multiple zones, guarantees high availability, further minimizing the risk of unplanned outages. By leveraging Google Cloud's capabilities, Solenis can ensure uninterrupted service for their customers, avoiding the negative impacts of downtime. This leads to improved customer satisfaction, increased productivity, and a stronger business reputation.

Furthermore, Google Cloud's secure-by-design infrastructure and robust security features have helped Solenis to improve its security posture. The company has been able to achieve compliance with a number of industry regulations, such as ISO 27001.

Since moving to Google Cloud, Solenis has experienced a 10x increase in response and transition times, leading to improved efficiency and productivity. Overall, performance improved by 35% utilizing Google Cloud resources for compute, memory, and storage. The company has also been able to optimize costs and improve performance by taking advantage of Google Cloud's committed use discounts (CUDs) and its high-performance compute and storage capabilities.

“Our migration to SAP on Google Cloud is a crucial milestone in our company's digital transformation journey, significantly reducing our technical debt and lowering operational costs,” Giboi said. “It not only enhances our agility and scalability but also positions us to better leverage innovative cloud technologies for future growth. Google Cloud is a reliable partner in our journey towards a more efficient, flexible, and sustainable technological future.”

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Eating our own dogfood: Building an AI-driven business at Google Cloud Consulting

Thu, 11 Apr 2024 16:00:00 +0000

At Google, we have a strong culture of dogfooding products — using and testing them internally to improve the experience for our own teams and customers. This practice is at the heart of Project LEAP, our internal effort within Google Cloud Consulting (GCC) to leverage Vertex AI in every part of our business.

This week at Next ‘24, we’re excited to share some of the ways we’re using Vertex AI products internally to create an AI-powered services business. Here are two of the exciting applications we’ve been developing on this journey.

1. GCC Assist: The AI-powered information hub

Google Cloud Consulting has a large corpus of rich data, including practical delivery experience, architecture models, best practice guidance, delivery guides, and templates. We have built an incredible repository of knowledge, but there was no simple way to quickly enable a user to tap into it and benefit.

Leveraging Vertex AI Vector Search and generative AI to summarize and synthesize information with simple natural language interactions was a huge opportunity to unlock the value of this data for everyone on our teams. The idea was simple: Create a centralized, AI-powered assistant that could intuitively understand and respond to natural language queries grounded in our data. With Vertex AI, GCC users now have a personal AI subject matter expert available on demand to help them find service delivery insights, best practice playbooks, service offerings based on customer use cases, and other key operational resources.

Already, we’re seeing how Vertex AI is helping our teams reduce the time it takes to search for information, prepare for customer meetings, and deliver our services. With this hub of information right at their fingertips, team members are now able to find answers and information faster, providing better support and more effective guidance to our customers.

One example of this is from Dan Roiter, a Director in Google Cloud Consulting: "I had an important meeting with a customer to discuss how we can enable a Teradata to BigQuery migration. I turned to GCC Assist with some of the inquiries I might have for a deep technical expert in this area. I was able to see sample projects, reference architecture, examples of benefits, and watchpoints. I was prepared for the customer meeting, and we were able to move forward with the program.”

2. GCC Scribe: Your generative AI contract drafting partner

Our teams also help scope customer engagements and develop statements of work (SOWs) to capture and document project plans with a customer. Research and drafting can take hours and often require several people with different areas of expertise.

To help us accelerate this process, we created GCC Scribe — a Vertex AI Vector Search powered tool to help teams write and deliver contracts and SOWs faster. With Scribe, users can search for SOWs using natural language to describe specific implementation scenarios and generate an SOW draft based on templates and previously approved examples. Scribe also provides automated contract review, which uses AI to review a contract before a user submits it, helping to flag issues and accelerate the approval process.

Challenges on the journey

We know from speaking with our customers and through our own everyday experiences that testing gen AI is usually rapid, helping to deliver quick proof-of-concepts. However, developing meaningful, production-ready gen AI applications requires building useful features with a clear impact and careful considerations around data quality, data availability, security, and privacy.

As we developed the Assist and Scribe applications, we encountered several key challenges that we encourage you to keep in mind as you start implementing your own AI projects.

Data preparation: The fuel for great gen AI experiences is rich and relevant data. We partnered with upstream data owners, consume the right data, normalize it, and augment it with metadata for accurate citations. This assessment helped show current and future opportunities for the data.
App vs. platform: To maximize the long term value, we decided to take a platform approach to building new apps rather than building them in isolation. Data is ingested once and then made available for multiple purposes and use cases. While the platform approach requires some upfront planning, it ultimately can help accelerate future feature development.
Personalization at scale: Every day we see new innovation emerging in interfaces, such as gen AI-powered chat features. Within Assist, users can click different buttons to ask questions within certain domains. This allows us to (1) set user expectations, (2) provide prompt engineering for each data set, and (3) provide a simple level of access control based on role type.
Security and privacy considerations: When it comes to data and AI in enterprise applications, security and privacy are critical concepts, which need to be considered in the early stages of planning. Addressing these issues from the beginning can help ensure that security and privacy are a fundamental part of designing features and making architectural decisions.

Our biggest takeaways from Project LEAP

Based on our experiences with implementing AI at GCC, the biggest lessons we learned along the way came back to the same central themes: data challenges, the importance of user-centric design, and truly understanding the considerations specific to AI in professional contexts.

Here are four recommendations that can help you navigate the path to successful Gen AI implementations:

Always do live demos and seek early feedback. “In theory” doesn’t get you far in AI deployments. Real, in-person and virtual demos are key for gaining executive buy-in and collecting accurate feedback about ideas or new product features.
Talk to your users. Good user experience is all about understanding the people using a product. Make sure you establish processes for users to provide constructive feedback and enable them to test things for themselves — even if it’s a work in progress.
Invest and take the first step. What you build now with gen AI is just a starting point, but it will be worth the investment. Your success at building with and embracing gen AI will likely be a differentiator in the future — the sooner you get started, the better.
Solve for yourself, solve for others. Every project is a white-label opportunity in another department, division, or company. If you build something useful for yourself, you will have opportunities to expand it.

We’re excited to share more at Google Cloud Next and stay tuned for future posts that dive into the technical implementation and underpinnings for these two solutions.

Within Google Cloud Consulting, we provide guidance to customers on how to test and productionize GenAI use cases with Vertex AI. We know every customer is on their own cloud journey and we want to meet you exactly where you are. Learn more about how Google Cloud Consulting can help you learn, build, operate and succeed.

^{Thank you to Kaitlin Ardiff, Gautam Bajaj, Daniel Massarsky, Robin Singh, Crispin Velez, and Vineet Verma for their leadership and collaboration on these initiatives and Sunil Rao & Lee Moore for their enthusiastic sponsorship of innovation and staying on the cutting edge with Google Cloud AI products.}

Introducing Isolator: Enabling secure multi-party collaboration with healthcare data

Thu, 11 Apr 2024 16:00:00 +0000

With more than a decade of experience building AI and machine learning models, Google Cloud understands both the benefits and challenges that await those looking to take advantage of these new technologies. Organizations around the world are now looking to AI solutions to solve some of their thornier problems, and to do so they need a safe, secure way when testing or developing products using important data.

In healthcare and life sciences in particular, generative AI has shown how it can have a significant impact. This includes improving access to information and insights, reducing administrative burdens for clinicians, and accelerating the pace of drug discovery and clinical trials.

Today at Google Cloud Next, we are announcing the availability of Isolator, a solution for secure infrastructure and data processing that adds a vital layer of protection to healthcare data used in collaborations between parties. Isolator can enable multi-party collaboration on work that involves handling raw and unprocessed, sensitive, and regulated data in an isolated, private, and compliant environment on Google Cloud.

Isolator can empower multiple parties to partner on sensitive data collaborations much more easily than before, and we expect cross-industry interest in how Isolator can help spur creative technology solutions. Some examples that Isolator can help with include:

Building custom models: Isolator can help develop custom models using Vertex AI and our foundational models that automate high-toil, administrative tasks such as writing discharge summaries.
Leverage complex data: Isolator can advance adoption of our Medical Image Suite, giving researchers and data scientists the efficiencies to improve workflows, and at a lower cost than keeping data on-premise.
Discovering new treatments: Isolator can be used with our Multiomics Suite to simplify drug discovery and clinical trials by streamlining workflows, enabling safe multi-party collaboration, and cutting development timelines.
Scaling advanced data analytics: Isolator can assist in reducing costs and improving results by moving and transforming data from siloed, on-premise databases to feature-rich and massively scalable analytic and data processing services such as Healthcare Data Engine and BigQuery.
Collaborating on research: Isolator is especially good at helping to build, package, and share data sets for use in model development and research. It can assure the integrity of data, and it can bring transparency into all actions taken against a data set.

Available through Google Cloud’s Consulting Services, customers and their partners can use Isolator to set up an isolated instance within their IT environment that allows them to work with sensitive healthcare data workloads while maintaining privacy, security, integrity and traceability.

It also can aid our customers when migrating their workloads to Google Cloud, and can help establish the secure boundaries necessary to create strong AI and machine learning models by securing data end to end.

At its core, Isolator is an environment built with Chrome Enterprise Premium, Google’s industry-first, widely-adopted Zero Trust technology. Coupled with other features built into Google Cloud, including VPC Service Controls, Chrome Enterprise, and encryption at-rest, in-use, and in- transit, Isolator can provide identity-based access that prevents data from leaving the secured Isolator environments — without requiring customers to set up and deploy special devices, data loss prevention controls, or VPN connections.

Isolator can also work with technology such as Google Confidential Space, which offers encryption for data in use with strong hardware isolation and remote attestation capabilities for even greater control.

Importantly, Isolator also comes built with detection and alerting capabilities that log data access activity, detect security misconfigurations, and report violations. This can help ensure customer security teams have full control over who is granted access to their sensitive healthcare data.

In just a few clicks, customers can provide secure access to data and machine learning resources on Google Cloud for any collaborator, anywhere in the world, and from any device, as long as the customer organization’s security policies are continuously met. If the security of an environment protected with Isolator changes, access to data is suspended until the necessary protections are restored. Isolator is built on top of Google Cloud Services and inherits controls and assurances described on our Compliance page.

At Google Cloud, we believe that sharing our knowledge and capabilities to build AI boldly and responsibly will benefit organizations and society as a whole. That approach helps drive our desire to create solutions such as Isolator. The power that safe and secure collaboration can provide to organizations working in healthcare (and beyond) is transformative and one of the main reasons why healthcare providers, payers, and researchers have put their trust in Google Cloud.

For more information on how to get started with Isolator today, please contact a Google Cloud sales specialist through our Google Cloud Consulting Services

New Google Cloud Consulting programs designed to accelerate your cloud journey

Wed, 10 Apr 2024 16:00:00 +0000

At Google Cloud Consulting, we are committed to helping our customers learn, build, operate, and succeed, wherever they are in their cloud journey. We are energized by the transformative potential of generative AI, and while it is getting a lot of attention, the cloud infrastructure that powers it remains essential.

Through thousands of customer engagements across the globe, we’ve found that knowledgeable staff, a strong technology foundation, and trusted guidance leads to innovation. I'm thrilled to announce new Google Cloud Consulting programs that will help meet our customers’ needs today as they bring their new ideas to life.

Learning fuels results

Our mission at Google Cloud Consulting is to make it easy for anyone, anywhere to learn the skills they need to succeed with Google Cloud technology. Learning and enablement are key parts of Google's core mission to organize the world’s information and make it universally accessible and useful. We’re excited to help organizations accelerate their learning journey and use gen AI to transform business functions. Highly trained organizations result in more successful cloud migrations and more confident workforces – so we’re betting big on learning as a business driver for our customers this year.

Gen AI requires skills most organizations are just now learning and many don’t have the in-house expertise to teach their workforce. Some 87% of the C-suite say they’re struggling to find talent with AI skills, and 77% say AI is disrupting their business strategy.

At Google Cloud, we want to change that dynamic. To show our commitment to expanding customers’ skills and knowledge, Google Cloud is offering no cost, on-demand training for our top customers, including new AI training for every member of your team. Customers will receive no-cost access to generative AI training led by a virtual instructor and our full catalog of on-demand training on Google Cloud Skills Boost, which includes freshly launched new gen AI skill badges. These three new badges showcase an individual’s applied and assessed abilities with Google Cloud concepts, products, and services:

To learn more, please connect with your account team.

Simplified solutions for real world impact

At Google Cloud Consulting, we are committed to solving our customers’ complex problems with business-focused solutions. To help you use gen AI to deliver results and overcome common roadblocks, we've teamed up with Google product experts to curate end-to-end solutions, both technology and services focused. You can leverage complete solutions to known challenges across developer productivity, customer service modernization, marketing, digital commerce, website modernization, and back office business applications — all expertly designed and delivered in collaboration with our partners to fit your specific industry needs.

We’re also making it easier to engage with Google Cloud Consulting. We will be rolling out an update to our credits program that provides broader access to the full GCC Services catalog without impacting any of your existing incentives. This simplified experience will increase flexibility for our customers and provide greater visibility into real-time spending insights with guidance directly from Google Cloud experts to help them get the most out of their services strategy.

Partner to make it real

Leveraging cloud technology and effectively integrating gen AI to transform your business takes teamwork and demands a robust foundation. That's why we're investing heavily in core infrastructure, ironclad security, and upskilling our customer-facing teams, especially within our robust partner ecosystem. At this year’s Next, we’re excited to announce that Delivery Navigator is now GA.

We initially unveiled Delivery Navigator at Google Cloud Next ’23 as a way for us to jointly create consistent, repeatable, agile, and high-quality experiences for our customers. And now we are ready and excited to bring the rest of our partner ecosystem this library of transformation methods, which are based on our experience running thousands of cloud projects.

We know that success in the cloud comes from close collaboration between our customers, Google Cloud Consulting teams, and our partners. Delivery Navigator is just one of the ways we’re helping our partners be successful collaborators, and our customers will benefit from these changes with more seamless and excellent delivery. By combining the expertise of Google Cloud and our partners, we provide customers with the best of both worlds: access to Google's cutting-edge technology and the specialized knowledge and experience of our partners.

Integration of gen AI into daily operations isn't just theory for us at Google Cloud Consulting, it’s practice. Our internal adoption of Gemini and other gen AI tools have transformed our own operations. We are excited to help our customers do the same, ensuring you have the right mix of business and technical strategies to make your cloud transformation a reality.

Ready to learn more? Discover how Google Cloud Consulting can help you learn, build, operate and succeed.

^{1. EdX Enterprise, ‘Navigating the Workplace in the Age of AI, 2023 Whitepaper}

Delivery Navigator: Now generally available to our services partners

Wed, 10 Apr 2024 15:00:00 +0000

Navigating the complexities of cloud migration and transformation is akin to exploring uncharted territories. Many businesses, just like yours, face challenges that divert their focus from core objectives to the intricacies of accessing cutting-edge tools and methodologies. But what if there was a way to simplify this journey, ensuring that your focus remains undiluted on what truly matters?

That’s why we’re announcing the general availability of Google Cloud Consulting’s Delivery Navigator to all of our qualified services partners. Delivery Navigator revolutionizes the way cloud projects are delivered, serving as a comprehensive platform that provides architectural and design guidance for Google Cloud solutions. It goes further, offering detailed, actionable implementation instructions for specialized areas like AI Foundations and Healthcare Data Engines, for example. We initially unveiled Delivery Navigator at Google Cloud Next’ 23 as a way for us to jointly create consistent, repeatable, agile, and high-quality experiences for our customers. And now, we are ready and excited to bring this library of transformation methods, and much more, to the rest of our partner ecosystem based on our experience running thousands of cloud projects.

Start by checking out this clip to learn more about how Delivery Navigator has helped optimize Google Cloud Consulting’s business and what it can do for your organization as well.

Building a new cloud methodology community, one partner at a time

Delivery Navigator, a combination of Google’s technology and implementation methodologies, started as an internal platform designed to help Google Cloud Consulting customers transform, create, and innovate on Google Cloud. After an initial private preview of the platform to our partner community last year, we are ready to open up Delivery Navigator to the rest of our qualifying partners that have at least one Partner Specialization.

This exciting milestone advances our vision of establishing Delivery Navigator as a vibrant cloud delivery methodology community — with rigorous privacy and security controls — that will include our partners and, eventually, our customers. We see the platform as a differentiated opportunity to collaborate and drive continuous value together.

Keeping practitioners focused on driving value and innovation

We have one goal: giving time back to our partners and customers to focus on what’s important to their businesses by providing the latest tools, techniques, and ways of working on the cloud in a single place, right at practitioners’ fingertips. Delivery Navigator is more than just a tool – it's a comprehensive ecosystem designed to streamline your cloud migration and transformation journey.

Built on Google Cloud Consulting's project management best practices, Delivery Navigator offers a holistic approach that enhances efficiency, elevates quality, and mitigates risk. Imagine having a tailored project plan on hand at any time, complete with technical steps, effort estimates, skill requirements, and template-based deliverables. In addition, Delivery Navigator is already integrated with project management tools like Jira, Smartsheets, Asana, and more. This efficiency helps free your engineers and project managers from tedious research and administration, empowering them to focus on understanding and delivering solutions that transform your customers' experiences.

Delivery Navigator offers a number of benefits for Google Cloud services partners, including:

Streamlined efficiency: Accelerate project delivery with structured, easy-to-follow instructions and resources built in context of the structure of the plan.
Enhanced quality: Leverage Google Cloud's best practices, rooted in proven experiences, for outcomes that not only meet but exceed customer expectations.
Minimized risk: A clear, structured approach that anticipates and mitigates potential challenges, along with structured guidance, helps you avoid common pitfalls, reduce rework, and keep your projects on track.
Increased customer satisfaction: Deliver projects that resonate with your clients by addressing their major challenges and solving their core pain points, enhancing their satisfaction and loyalty.
Expertise development: Collaborate with us on expanding the platform's knowledge base through co-authoring and co-branding opportunities.

Let’s get started, together

Ready to transform how you deliver Google Cloud projects? If your organization holds at least one Partner Specialization in a solution area, you're invited to check out our getting started guide today, in which you will find more information about the platform and the onboarding process, and join a community dedicated to excellence in cloud services.

Dive into the future of cloud services with Google Cloud Consulting and Delivery Navigator — where your cloud potential is unlocked, project by project.

Maximize your cloud potential at Next ‘24: Lead with learning at any skill level

Wed, 13 Mar 2024 16:00:00 +0000

This April, what happens in Vegas could raise the stakes on your cloud expertise!

Join us April 9-11 at Google Cloud Next ‘24 in Las Vegas, where Google Cloud Consulting will offer three epic days of learning opportunities for attendees at any stage in their cloud journey – from curious beginners to advanced professionals, developers and partners to CxOs. It’s been exciting to build programming that can have a direct, positive impact on Next attendees professional journeys, and we have so much planned that you don’t want to miss. Get hands-on experience with the latest technology, explore live workshops, acquire skill badges, collect unique swag, watch demos on fresh innovations, perform challenges, connect with experts in the community, and more. Phew! Secure your ticket and register today.

Got your ticket and ready to go? Check out some highlights of the learning experiences you can expect at Google Cloud Next.

Earn our new generative AI skill badge (and swag!)

Skilling up in gen AI? Now that’s a safe bet. The no-cost introductory Prompt Design in Vertex AI skill badge gives you hands-on experience building cloud solutions faster with gen AI while earning a valuable Google Cloud credential. Complete the introductory Prompt Design in Vertex AI skill badge before or during Next ‘24, and visit the Learning and Certification booth to pick up your limited-edition swag. Make sure to share your digital #GoogleCloudSkillBadge on your socials to highlight your achievement.

Identify your Cloud Type

Have you ever pondered if you should set sail as a Python Pirate, or if you’d be better off finding your focus flow with a rubber duck? Great news — there’s a way to find out. Discover the skills that make you shine by taking our brand new “What’s my Cloud Type?” quiz at the Learning and Certification booth. It’s both entertaining and grounded in real insights to help you make informed decisions about your learning journey. After answering a few fun questions about your interests and working style, you’ll find out your Cloud Type — and receive recommendations for the relevant Google Cloud skill sets and personalized learning paths that align to it. Whether you lean towards realistic or artistic, prefer to investigate or innovate, there’s something for everyone to help tailor an ideal learning experience. You’ll also get a limited-edition pin to show your cloud skills with pride.

Power up your potential with a training workshop

No matter your learning level, we’ve got a session to help you enhance your career. Individuals who get Google Cloud trained and certified report boosts in salary — and in feeling more confident about their work¹. So invest in yourself and consider prioritizing a certification training workshop. There are a variety of ways to learn, including hands-on learning, gamified activities, speaker presentations, and more. New to data analysis and hoping to practice querying large datasets? Wanting to improve and implement your advanced gen AI abilities by creating a generative chat app? All that and more is waiting for you to explore in the session library. Check it our and book your Tech Training session seat today – they’re filling up fast!

Join a Cloud Learning breakout session

Want to understand how training and certification can transform your company's culture? We’ve got you. Get the lowdown on how to prepare for the most popular Google Cloud certification exams, realize the ways gen AI can enhance your team’s learning journey, gain insight into the latest learning resources from Google Cloud, and more. You won’t want to miss our customer sessions, where you’ll hear real stories about the positive impact that cloud learning has had on their businesses. Don't forget to check out the Cloud Learning and Certification playlist and mark your calendar for the sessions that catch your eye.

Let inspiration strike at a Lightning Talk

As industries focus more on skills², it becomes crucial to understand how to stay up to date — and how to translate your skills into career growth. Google Cloud Learning Services will be holding two Lightning Talks on how to own your professional narrative. Unsure how to differentiate your résumé? “Stand Out with Google Cloud Credentials” will show you how to turn your Google Cloud Credentials into a powerful currency. Wondering how you may need to adjust your approach to learning as technology advances at a rapid pace? “Need for Speed: Skilling up in the World of Cloud and AI” illuminates the importance and process of evaluating and re-upping your existing skills in this fast-paced world of AI and cloud technology. Learn more and discover even more Innovators Hive Lightning Talks in the session library.

Advance your skills and get limited-edition swag

Who doesn’t love swag? Demonstrate your Google Cloud prowess by earning any of these three skill badges from now until Next:

You’ll get a digital badge on your developer profile that you can share online to showcase your commitment to #GoogleCloudLearning. Then, when you’re at Next in Las Vegas, swing by the Learning and Certification booth and show us your developer profile to snag a limited-edition pin.

Get guidance and greetings at the Learning and Certification booth

Whether you’ve got questions about the latest updates to learning content or are on the hunt for stickers, the Learning and Certification booth is your destination. Drop by to chat with training experts, interact with the freshest learning materials and credentials, and of course, take your Cloud Skills Quiz! We’re also debuting our Learning Library: a collection of certification preparation and Googler authored generative AI books that you can check out and receive a discount to purchase online. And don’t forget to snap a selfie at our photo wall!

Test your talents with the Persona Quizathons

Got 10 minutes during Next? Play the Google Cloud Quizathon in any of the seven Persona Areas in the Innovators Hive. You’ll test your knowledge of basic Google Cloud concepts in a fun, quiz-based game, and score a digital badge and physical swag to show off your expertise.

Network and find the buzz at the Community Hub

Make connections at the Community Hub in the Innovators Hive. Connect with developers and Google Cloud Innovators to chat and learn about different Google communities and find the right one(s) for you. You’ll also find persona-based demos, access to experts, meetups, and more. Stop by anytime – there’s always something hopping at the Hub.

Can’t wait to get started? Sign up for the Google Cloud Community and join the conversation today.

Engage with experts at the Google Cloud Consulting booth

Head to the Showcase and visit the Google Cloud Consulting booth for personalized consultations with product and solution experts and industry experts. Want tips on transforming your business? You can learn how to harness the power of advanced analytics to maximize your data insights, or explore integrating cutting-edge AI solutions to automate processes, improve decision-making, and enhance customer experiences. Don't miss this opportunity to connect with leaders in cloud technology and gain the insights you need to drive your business forward.

Relax and revel in the Champions and Certified Lounge

Exclusively for Champion Innovators and the Google Cloud Certified community, come visit the lounge in the Innovators Hive to network, deep dive into discussions, or kick back and snack. You’ll find light refreshments, curated programming and meetups, and a photowall to capture and share your experiences. There will also be a few special surprise activities to help you unwind and bond with your fellow lounge-goers. Make sure to identify yourself as a Champion Innovator or Google Cloud Certified when you register for Next on your Profile page for easy lounge entry.

We’re abuzz with excitement in anticipation of our biggest event of the year! Are you ready to raise your skill stakes and ante up your investment in your professional future? Come learn with us at Next in Las Vegas — we hope to see you there! Register today.

^{1. Based on survey responses from the 2020 Google Cloud certification impact report}^.
^{2. From this 2022 Deloitte article}

Why mainframe migration is more than technology transformation

Fri, 16 Feb 2024 17:00:00 +0000

British author and novelist L.P. Hartley once said, “The past is a foreign country: They do things differently there.” This sentiment is especially true in the realm of mainframe migrations, which often trigger unforeseen changes across an organization.

Processes established in mainframe environments tend to be delivered as centralized internal services with critical capabilities, such as maintenance, security, and support. However, these centralized processes and delivery are also bound to transform when migrating applications and workloads to cloud environments.

Centralized delivery has proven effective at lowering costs through consistency, shared engineering resources, bulk discounts, and specialized understanding of organizational requirements. At the same time, finite resources also hindered scalability, flexibility, and speed; any initiative that tangentially touched the mainframe had to be coordinated centrally.

But as cloud migrations near the finish line, many organizations suddenly realize they will need to replicate the same work once carried out by their mainframe teams — and they have new power to make changes. Here, deep expertise from a cloud provider or partner can prove invaluable for helping organizations get the most out of their transformations.

At Google Cloud Consulting, we've helped organizations navigate this complex journey countless times, witnessing firsthand that transformation is not just about technology but also people and processes. In this post, we’ll share three areas where you can expect the biggest impact and change when migrating from mainframes to the cloud.

Maintenance: The difference is in the design

We frequently advise customers to design away the need for processes, so they don’t become a bolt-on activity that the new cloud team needs to worry about. For example, opting for a Google Cloud managed database, such as AlloyDB, CloudSQL or BigQuery, can remove many of the utilities and maintenance tasks associated with on-premises databases or data stores. Instead of worrying about nightly backups or annual database reorganizations, these processes are automated and managed by Google Cloud.

Similarly, Google Cloud can automatically perform OS upgrades and patches by rotating GKE instances on a regular basis (e.g., 30-45 days). Organizations can define this in their policies and integrate rolling out the new OS images into their CI/CD pipelines. In this way, these processes become part of the normal development lifecycle rather than a separate activity that must be monitored and managed.

Of course, teams will still need to test backup and emergency restore processes regularly. However, testing doesn’t have to be particularly onerous. The intrinsic network isolation combined with on-demand capacity means it is quite possible to spin up a disaster recovery environment, restore a database, run the tests, and tear it down again — all without any disruption to the production environment.

Compared to the centralized processes they replace, maintaining and updating cloud-based applications generally takes a fraction of the work and time associated with the same tasks for on-premises solutions.

Security: Everyone gets a key (well maybe not)

Dealing with security is often another challenging transition during migrations. Google Cloud offers encryption at rest by default, but in isolation, that is not always enough. There are additional steps, such as rotating keys, to consider. With the move to a cloud environment, rotating the keys becomes the responsibility of the organization.

Where a mainframe may have been grandfathered in with the minimum acceptable solution, many organizations take the opportunity to step up their security controls when migrating to the cloud, which can create new burdens. In mainframes, the same key might be used for all data. In the cloud, applying the same policy to everything, everywhere, is illogical.

A common pattern we see for success is pushing the responsibility of rotating keys down to each product or application owner. This allows for different rotation policies based on the data; keys for data that require the highest levels of PCI and PII protection might be rotated every 90 days, while public data may be every 12 months. This pattern also shrinks the blast radius related to compromised keys. The goal is to tailor the policy for the data and any local laws, improving the overall security posture with nominal additional effort.

Similarly, cloud compliance is a crucial issue to address. Compliance audits are time-consuming, but they also play a critical role in helping organizations ensure transparency and safeguard their data and cloud assets. The trick to a successful audit is supporting policies and documentation with an electronic trail that shows compliance. The beauty of the cloud is that it’s easy to layer organizational policies on top of existing cloud documentation and use logs from products and services as proof of meeting requirements.

Support: New responsibilities, better solutions

There’s nothing worse than a 2AM call because something has failed (bad) or is limping along (worse). The tightly integrated nature of the mainframe used to mean that support primarily fell to a core on-call team with the skills and security permissions to troubleshoot mainframe problems from the ground up.

With a migration to Google Cloud, we are responsible for providing the instructure, but we have little to no insight into what is being executed. Therefore, support will shift to individual product teams — a new responsibility, but one that comes with a lot of help.

Whether it is a Single Pane of Glass or a “Single Glass of Pain”, Google Cloud Operations contains a fully integrated suite of metrics to assist troubleshooting. These metrics can be optimized with plug-ins to reflect an organization’s particular technology stack.

In addition, the cloud also offers new options for resolving issues — ones that don’t require debugging on a call in the middle of night. For instance, it’s now possible to simply isolate the instance having the problem from the cluster and spin up a replacement in seconds, leaving customers to get on with their lives and teams to solve problems in the morning with a cup of coffee.

Final thoughts

Embarking on a mainframe migration journey is both challenging and rewarding. Taking on new roles and responsibilities can be exciting, but it’s likely to be a bumpy transition for existing teams that have spent decades honing their skills in specific roles. Organizations should accept this up front and plan accordingly, eliminating as much as possible through design and automation.

At Google Cloud Consulting, we understand the intricacies of this process. Our team is dedicated to helping clients navigate these changes, leveraging our deep expertise to streamline the transition, automate processes, and empower organizations to embrace the full potential of the cloud.

Read more about how Google Cloud approaches mainframe modernization, or drop us a line if you want to chat with one of our experts. Our invitation is open: connect with us at Google Cloud Consulting to explore how we can tailor a migration strategy that aligns with your organization's unique needs and goals. Together, we can turn the challenge of mainframe migration into an opportunity for growth and innovation.

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