The microservice architecture has become increasingly popular over the past decade. Its key benefits include significantly improving the developer experience and accelerating software delivery. Sadly, however, microservices have often been widely misunderstood and used inappropriately. As a result, many organizations have struggled to benefit from their adoption. To prevent this from happening, 11 development and architecture rules (a.k.a. best practices) have been defined.

Architecture and components of PSSO; implementation requirements and prerequisites; configuration steps and best practices; troubleshooting common PSSO issues.

AI Hypercomputer is a revolutionary system designed to make implementing AI at scale easier and more efficient. In this session, we’ll explore the key benefits of AI Hypercomputer and how it simplifies complex AI infrastructure environments. Then, learn firsthand from industry leaders Shopify, Technology Innovation Institute, Moloco, and LG AI Research on how they leverage Google Cloud’s AI solutions to drive innovation and transform their businesses.

JavaScript gets a lot of flak for not being strongly typed. But if you’re running JavaScript in production today, you don’t need to wait for runtime errors to catch problems. TypeScript has taken JavaScript from a loosely typed language, where a variable can change from a string to a number without warning, and made it strongly typed. Now Zod and Effect are here to tame even the wildest unknown parameters from your users. We’ll demonstrate using these tools in an application and we’ll deploy that application to Google Cloud.

As AI adoption accelerates, many enterprises still face challenges building production-grade AI systems for high-value, knowledge-intensive use cases. RAG 2.0 is Contextual AI’s unique approach for solving mission-critical AI use cases, where accuracy requirements are high and there is a low tolerance for error. 

In this talk, Douwe Kiela—CEO of Contextual AI and co-inventor of RAG—will share lessons learned from deploying enterprise AI systems at scale. He will shed light on how RAG 2.0 differs from classic RAG, the common pitfalls and limitations while moving into production, and why AI practitioners would benefit from focusing less on individual model components and more on the systems-level perspective. You will also learn how Google Cloud’s flexible, reliable, and performant AI infrastructure enabled Contextual AI to build and operate their end-to-end platform.

Unlock the power of generative AI with retrieval augmented generation (RAG) on Google Cloud. In this session, we’ll navigate key architectural decisions to deploy and run RAG apps: from model and app hosting to data ingestion and vector store choice. We’ll cover reference architecture options – from an easy-to-deploy approach with Vertex AI RAG Engine, to a fully managed solution on Vertex AI, to a flexible DIY topology with Google Kubernetes Engine and open source tools – and compare trade-offs between operational simplicity and granular control.

The rise of AI-powered code generation tools presents a compelling alternative to traditional UI prototyping frameworks. This talk explores the question: Is it time to ditch the framework overhead and embrace core web technologies (such as HTML, CSS, JavaScript) for faster, more flexible prototyping? We’ll examine the trade-offs between structured frameworks and the granular control offered by a “bare metal” approach, augmented by AI assistance. Learn when leveraging AI with core tech becomes the smarter choice, enabling rapid iteration and bespoke UI designs, and when frameworks still reign supreme.

In today's digital landscape, organizations are sitting on untapped potential within their cloud environments. While many enterprises have made the initial move to Google Cloud, true value creation comes from modernizing applications and operations to fully leverage cloud-native capabilities. The journey typically unfolds across multiple phases and each phase can compound the benefits. But don't let the complexity of modernization hold you back.

This Session is hosted by a Google Cloud Next Sponsor.
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This session dives into building a modern data platform on Google Cloud with AI-powered data management. Explore how to leverage data mesh architectures to break down data silos and enable efficient data sharing. Learn how data contracts improve reliability, and discover how real-time ingestion empowers immediate insights. We'll also examine the role of data agents in automating data discovery, preparation, and delivery for optimized AI workflows.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

Discover the cutting edge of foundation model development with JAX on Google Cloud. This session will showcase the latest advancements in the JAX ecosystem, including optimized performance on TPUs and GPUs. Explore new, high-performance models powered by MaxText and MaxDiffusion, delve into enhanced JAX libraries and Stable Stack packages, and learn about advanced diagnostics tools. Gain insights into how leading customers and partners are leveraging JAX on Google Cloud to build and deploy next-generation foundation models at scale.

Struggling with multicloud networking complexity? Equinix and Uber reveal the critical network architecture strategies to overcome today’s challenges. Discover proven adoption tactics and essential multicloud networking capabilities for seamless, cost-effective multicloud success. Learn how Uber, leveraging Equinix’s interconnected global data centers and Network-as-a-Service platform, achieved rapid, flexible, and efficient data migration to Google Cloud. Don’t let network limitations hold back your multicloud potential.

This Session is hosted by a Google Cloud Next Sponsor.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

This session shows how engineers can use Gemini Cloud Assist and Gemini Code Assist to speed up the software development life cycle (SDLC) and improve service quality. You’ll learn how to shorten release cycles; improve delivery quality with best practices and generated code, including tests and infrastructure as code (IaC); and gain end-to-end visibility into service setup, consumption, cost, and observability. In a live demo, we’ll showcase the integrated flow and highlight code generation with GitLab and Jira integration. And we’ll show how Gemini Cloud Assist provides deeper service-quality insights.

This session provides an in-depth look at the Google infrastructure that powers our most demanding AI workloads. We’ll explore the journey from custom silicon, high-bandwidth networking and storage to the software frameworks that enable efficient, large-scale training and inference with industry-leading goodput and uptime across the largest GPUs and TPU clusters. Learn how Google’s unique approach to system design and deployment enables customers to effortlessly achieve Google-level performance and scale for their own applications.

Explore the future of data management with BigQuery multimodal tables. Discover how to integrate structured and unstructured data (such as text, images, and video) into a single table with full data manipulation language (DML) support. This session demonstrates how unified tables unlock the potential of unstructured data through easy extraction and merging, simplify Vertex AI integration for downstream workflows, and enable unified data discovery with search across all data.

Unlock the full potential of Compute Engine for all your applications. This session delivers actionable strategies and best practices to optimize cost, reliability, and management for cloud-first, AI, machine learning, high performance computing, enterprise, and stateful workloads. We’ll share recently released features within Compute Engine to maximize return on investment for each specific application type.