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.

APIs dominate the web, accounting for the majority of all internet traffic. And more AI means more APIs, because they act as an important mechanism to move data into and out of AI applications, AI agents, and large language models (LLMs). So how can you make sure all of these APIs are secure? In this session, we’ll take you through OWASP’s top 10 API and LLM security risks, and show you how to mitigate these risks using Google Cloud’s security portfolio, including Apigee, Model Armor, Cloud Armor, Google Security Operations, and Security Command Center.

Bring your laptop and join us for an interactive demo on how to apply large language models (LLMs) from the Vertex AI Model Garden to a business use case, and learn about best practices for monitoring these models in production. We’ll go through an exercise using Colab Enterprise notebooks and learn how to use out-of-the-box tools to monitor RED (rate, error, duration) metrics, configure alerts, and monitor the rate of successful predictions in order to ensure successful use of a Vertex AI model in production.

This panel explores the potential of cloud technologies for operational technology (OT) and the critical need for proactive cybersecurity measures. The convergence of OT and IT, driven by cloud adoption, presents both opportunities and challenges. Panelists will examine the benefits of cloud-based OT, such as increased efficiency, scalability, data-driven insights, and resilience, along with the opportunity to build with security in mind.

Firestore with MongoDB compatibility is a serverless database service  designed to maximize scalability, high availability and performance without the hidden costs of capacity planning. This session demonstrates the new Firestore with MongoDB compatibility capabilities and discusses how Dialpad has built an Ai-powered customer communications platform leveraging Firestore over the last 14 years to grow a successful, performant, reliable business.

Learn how to manage security controls and licenses for thousands of users, and tie it all together with APIs. We’ll show you ways to manage developer access more efficiently, build custom management integrations, and keep your CISO happy at the same time. We’ll also demo the new Gemini Code Assist integration with Apigee, which lets developers use Gemini Code Assist chat to generate context-aware OpenAPI specifications that reuse components from other APIs in their organization for efficiency and reference organizational security standards.

Join this session to learn best practices for optimizing your data and analytics costs. Discover new BigQuery capabilities that simplify workload management and provide greater cost controls and adherence to best practices. BigQuery customers Just Eat Takeaway and Trendyol will share their BigQuery migration journeys, scaling strategies, and how they used workload management and optimization tools to improve their return on investment (ROI).

Join us for an in-depth session on Firebase Genkit, an open source framework that simplifies the development of AI-powered applications. Discover how to use the Node.js and Go SDKs to build intelligent chatbots, multimodal content generators, streamlined automation workflows, and agentive experiences. We'll demonstrate how Genkit's unified interface seamlessly integrates Google's Gemini and Imagen models, self-hosted Ollama options, and a variety of popular models from Vertex AI Model Garden. 

Migrating from AWS or Azure to Google Cloud runtimes can feel like navigating a maze of complex services and dependencies. In this session, we’ll explore key considerations for migrating legacy applications, emphasizing the “why not modernize?” approach with a practice guide. We’ll share real-world examples of successful transformations. And we’ll go beyond theory with a live product demo that showcases migration tools, and a code assessment demo powered by Gemini that demonstrates how you can understand and modernize legacy code.

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.

Learn how to leverage Google Cloud’s innovations to build the most secure and compliant solutions with Confidential Computing, offloads, and Assured Workload capabilities.

Simplify real-time data analytics and build event-driven, AI-powered applications using BigQuery and Pub/Sub. Learn to ingest and process massive streaming data from users, devices, and microservices for immediate insights and rapid action. Explore BigQuery's continuous queries for real-time analytics and ML model training. Discover how Flipkart, India’s leading e-commerce platform, leverages Google Cloud to build scalable, efficient real-time data pipelines and AI/ML solutions, and gain insights on driving business value through real–time data.

Cloud marketplaces are projected to reach $85B by 2028, with over half involving channel partners (Source: Canalys). Google Cloud Marketplace offers a unique advantage for Channel Partners and ISVs to collaborate and succeed. Learn about the latest platform capabilities to drive deal velocity, new resell offerings and incentives designed to scale growth. Tap into new revenue through the channel and thrive in the evolving cloud marketplace.

Unlock the power of code execution with Gemini 2.0 Flash! This hands-on lab demonstrates how to generate and run Python code directly within the Gemini API. Learn to use this capability for tasks like solving equations, processing text, and building code-driven applications.

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!

Join us as we explore the exciting future of AI in gaming. This session will showcase how leading organizations are leveraging machine learning and other AI technologies to create next-generation games experiences. Discover how AI is shaping the evolution of gameplay, storytelling, and player interaction, and gain insights into the trends that will define the future of interactive entertainment.

Test your incident response skills in the Security Tabletop Workshop, an exercise that simulates a realistic cyberattack to assess and improve an organization’s security posture. Tabletop exercises are a critical component of any cybersecurity strategy, helping your team reduce the impact of cyberattacks, evaluate response strategies, address gaps, foster collaboration, and clarify roles during a crisis.

Take part in the new SOC Experience – a view into real-world attack scenarios. Learn about the latest hacker tactics and how Google equips cybersecurity teams with the data, AI, and scalable analytics to detect and remediate attacks.

In this session, you’ll learn how to build an AI agent using MongoDB Atlas on Google Cloud in just 15 minutes. We’ll cover how to embed and store your data along with vectors in a single database; build a vector search index and run search queries; and implement an AI agent.

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.

In today’s global media landscape, localizing video content is key to reaching wider audiences. This session demonstrates how to build a robust, end-to-end AI video dubbing pipeline on Google Cloud—covering video ingestion, transcription, translation, captioning, voice cloning, voice synthesis, and quality checks. We’ll explore an architecture that seamlessly integrates GCP products and best practices, highlight market opportunities, and address key challenges in AI dubbing. Attendees will learn how to streamline workflows so users can simply submit their video and receive fully dubbed results. Join us to see how Google tackles dubbing hurdles to maintain its leading edge in global content localization.

Debug Google Kubernetes Engine (GKE) apps like a pro! This hands-on lab covers using Cloud Logging & Monitoring to detect, diagnose, and resolve issues in a microservices application deployed on GKE. Learn practical troubleshooting workflows.

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!

Unlock your team's full potential with the power of Google Cloud training for partners. Explore the learning portfolio designed for partner practicioners including role-based learning paths, certification programs, skill badges, and more. Plus, we'll highlight the latest from the generative AI portfolio. Boost capabilities and drive customer success without compromising productivity!

Personalized predictions can be created by analyzing user clickstream data and using vector embeddings to capture the essence of an entity across multiple dimensions. This establishes relationships between users and items, revealing preferences and interests. BigQuery facilitates batch processing of vector embeddings, which are then fed into Spanner for efficient retrieval of these relationships via vector search. This enables real-time personalized recommendations with sub-ms response times. This solution offers accuracy, scalability, and real-time responsiveness.

Celebrate the journey at The Startup Founder's Toast & Ice Cream Social. Join us for an inspiring closing session at Next 25, where we'll share stories, deepen connections, and toast to the innovation, resilience, and bold ideas of visionary founders.

In this hands-on lab, you'll explore data with BigQuery's intuitive table explorer and data insight features, enabling you to gain valuable insights without writing SQL queries from scratch. Learn how to generate key insights from order item data, query location tables, and interact with your data seamlessly. By the end, you’ll be equipped to navigate complex datasets and uncover actionable insights quickly and efficiently.

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!

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.