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.
Visit your registration profile at g.co/cloudnext to opt out of sharing your contact information with the sponsor hosting this session.

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.

This talk explores a comprehensive key management strategy designed to safeguard your critical assets, now and in the future. We’ll focus on the foundation of modern key management, evolving cloud hardware security offerings, sovereign key management, and cloud key management. By combining the strengths of advanced hardware security module (HSM) technology, sovereign key management principles, and the flexibility of cloud environments, organizations can build a robust and future-proof security posture.

In today’s fast-paced market, data is key to innovation. This session explores how Apigee, combined with Google Distributed Cloud, enables organizations to unlock the value of their data, regardless of its location. Learn how to operationalize data across legacy systems, the cloud, and edge environments to build cutting-edge solutions like generative AI and advanced analytics. Discover how Apigee simplifies data accessibility and interoperability, accelerating your time to market and maximizing the potential of your data assets.

Build resilient, scalable applications that thrive in the face of increasing demands. Cloud SQL offers new features designed to optimize performance, availability, and cost efficiency for MySQL and PostgreSQL databases, managed replica pools, and connection pooling. Learn how to make downtime a thing of the past, implement advanced disaster recovery strategies, and maximize your application’s performance. Join our demo-packed session for a deep dive into these new Cloud SQL capabilities and best practices.

Build & deploy with Google Cloud Deploy! This hands-on lab equips you to create delivery pipelines, deploy container images to Artifact Registry, and promote applications across GKE environments.

If you register for a Learning Center lab, please ensure that you sign up for a Google Cloud Skills Boost account for both your work domain and personal email address. You will need to authenticate your account as well (be sure to check your spam folder!). This will ensure you can arrive and access your labs quickly onsite. You can follow this link to sign up!

Experience the future of AI with Google Cloud! Speak with customers who are building innovative AI solutions and learn directly from them. This experience offers a real-world look into "how we built it" discussions, giving you the chance to explore the possibilities. See detailed schedule of each timeblock here.

Scale your AI training and achieve peak performance with AI Hypercomputer. Gain actionable insights into optimizing your AI workloads for maximum goodput. Learn how to leverage our robust infrastructure for diverse models, including dense, Mixture of Experts, and diffusion. Discover how to customize your workflows with custom kernels and developer tools, facilitating seamless interactive development. You'll learn firsthand how Pathways, developed by Google Deepmind, enables large scale training resiliency, flexibility to express architecture.

Join an insightful fireside chat with Jeff Dean, a pioneering force behind Google’s AI leadership. As Google's Chief Scientist at DeepMind & Research, Jeff will share his vision on AI and specialized AI hardware, including Cloud TPUs seventh generation chip; Ironwood. What exciting things might we expect this to power? What drives Google’s innovation in specialized AI hardware? In this spotlight, we’ll also discuss how TPUs enable efficient large-scale training and optimal inference workloads including exclusive, never-before-revealed details of Ironwood, differentiated chip designs, data center infrastructure, and software stack co-designs that makes Google Cloud TPUs the most compelling choice for AI workloads.

This session provides a look into how Abridge built a secure and scalable inferencing platform on Google Kubernetes Engine (GKE). We’ll demonstrate how they leverage GKE fleets, Teams, Argo CD, and multi-cluster orchestration to manage and deploy inferencing workloads that span multiple clusters. View a live demo of a complete solution featuring a custom Argo CD plugin that simplifies cluster management and streamlines deployments for platform admins and application teams.

This session dives into the latest advancements in securing and managing your Cloud Run workloads at enterprise scale. Join us to learn about new features and techniques to meet the highest security standards, strategies for managing large-scale deployments, and solutions to common issues like IP exhaustion. Plus, one of our customers will share their firsthand experience managing a massive fleet of Cloud Run workloads.

In this session, we’ll explore Google’s latest developments in Google Kubernetes Engine (GKE) that enable unprecedented scale and performance for AI workloads. We’ll dive into how Anthropic leverages these capabilities to manage mega-scale Kubernetes clusters, orchestrate diverse workloads, and achieve breakthrough efficiency optimizations.

Is your platform ready for the scale of rapidly evolving models and agents? In this session, we’ll explore strategies for scaling your cloud native AI platform - empowering teams to leverage an increasing variety of AI models and agent frameworks. We’ll dive into tools and practices for maintaining control and cost efficiency while enabling AI engineering teams to quickly iterate on Google Kubernetes Engine (GKE). We’ll explore how NVIDIA NIM microservices deliver optimized inference with minimal tuning. 

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.

Learn how LG AI Research uses Google Cloud AI Hypercomputer to build their EXAONE family of LLMs and innovative Agentic AI experiences based the models. EXAONE 3.5, class of bilingual models that can learn and understand both Korean and English, recorded world-class performance in Korean. The collaboration between LG AI Research and Google Cloud enabled LG to significantly enhance model performance, reduce inference time, and improve resource efficiency through Google Cloud's easy-to-use scalable infrastructure