Good customer experiences are a crucial factor in business growth and efficiency. Learn how to deliver exceptional customer outcomes with Google's Customer Engagement Suite (CES). Powered by Gemini, CES combines advanced conversational AI with multimodal, omnichannel capabilities to enable faster, more personalized digital experiences. This session explores CES value for customers and partners, go-to-market strategies, and real-world success stories.

This session showcases how Gemini Code Assist revolutionizes end-to-end Java application development. Join us to learn how to accelerate each development stage – from backend to frontend and testing. Discover how to leverage Gemini code generation, completion, and debugging features. Explore how to enhance productivity and build robust, high-quality applications faster. And take away practical methods and techniques for integrating Gemini Code Assist into your workflow.

Cloud Run is an ideal platform for hosting AI applications – for example, you can use Cloud Run with AI frameworks like LangChain or Firebase Genkit to orchestrate calls to AI models on Vertex AI, vector databases, and other APIs. In this session, we’ll dive deep into building AI agents on Cloud Run to solve complex tasks and explore several techniques, including tool calling, multi-agent systems, memory state management, and code execution. We’ll showcase interactive examples using popular frameworks.

Want to build your own Gemini-powered applications? Bring your laptops. This technical session provides the interactive experience you need. We’ll guide you through practical examples and code snippets, and cover how to start creating with the Gemini SDK on Vertex AI.

To achieve your business goals while staying within the budget, it’s crucial to have complete visibility into your cloud spending, down to the specific applications and workloads. You need to know exactly where your money is going and how efficiently resources are being used, as well as opportunities to optimize spending. In this session, we’ll show you how to gain such knowledge and insights about your applications. We’ll explore how to use product dashboards and Gemini Cloud Assist to identify optimization opportunities. Leave with actionable strategies to maximize your cloud return on investment (ROI) and achieve your business goals.

Model Armor is designed to protect your organization’s AI applications from security and safety risks. In this session, we’ll explore how Model Armor acts as a crucial layer of defense, screening both prompts and responses to identify and mitigate threats such as prompt injections, sensitive data leakage, and offensive content. Whether you’re a developer looking to implement AI safety or a professional interested in better visibility into AI applications, Model Armor offers comprehensive yet flexible security across all of your large language model (LLM) applications.

This session offers a technical deep dive of the state-of-art AlloyDB AI capabilities for building highly accurate and relevant generative AI applications using real-time data. We’ll cover vector search using Google Research’s ScaNN index technology and cover how you can utilize Gemini from AlloyDB operators to seamlessly integrate into your application. Discover AlloyDB AI natural language feature, a new way to interact with databases and how it accurately and securely answers your questions. Also learn about the latest research between Google and NVIDIA on GPU-accelerated vector index builds in databases.

How can organizations create scalable, engaging communications that capture attention and break through all of the noise? In the session, explore how the newest Google Workspace app is empowering teams of all sizes to build dynamic video content with Gemini to connect with coworkers and partners in new ways and break through the noise. Explore the latest customer insights, experience Vids in action, and discover what’s next.

"Talk-to-Manuals" leverages Google Cloud's Vertex AI Search and Conversation, along with a multi-turn GenAI agent, to provide customers with easy access to vehicle information. The solution integrates and indexes diverse data sources—including unstructured PDFs and structured data from FAQs—for efficient retrieval. Semantic search, powered by LLMs, understands user intent, delivering highly relevant results beyond keyword matching. The interactive multi-turn conversation guides users towards precise information, refining queries and increasing accuracy. Data filtering based on vehicle attributes (model, year, engine type) combined with sophisticated GenAI-powered post-processing ensures tailored results. The successful implementation of "Talk-to-Manuals" significantly improved customer service by providing a more accessible and intuitive way for customers to find information.

This meetup is a space for developers actively working with any open-source AI libraries, frameworks, or tools, to share their projects, challenges, and solutions. Whether you're building with LangChain, Haystack, Transformers, TensorFlow, PyTorch, or any other open-source AI tool, we want to hear from you. This meetup will provide an opportunity to connect with other developers, share practical tips, and get inspired to build even more with open-source AI on Google Cloud. Come ready to contribute, and let's learn from each other!

Unleash the power of Gemini with Vertex AI Studio. This hands-on lab guides you through using Gemini for image analysis, prompt engineering, and conversational AI, all within a user-friendly interface. Learn to design prompts and generate content directly from the Google Cloud console.

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!

Concerned about AI hallucinations? While AI can be a valuable resource, it sometimes generates inaccurate, outdated, or overly general responses - a phenomenon known as "hallucination." This hands-on lab teaches you how to implement a Retrieval Augmented Generation (RAG) pipeline to address this issue. RAG improves large language models (LLMs) like Gemini by grounding their output in contextually relevant information from a specific dataset. Learn to generate embeddings, search vector space, and augment answers for more reliable results.

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!

Learn how "Project SEALD" – a collaboration between Google and AI Singapore – is building LLMs for the region. Discover why cultural context matters and how you can implement similar solutions.

Join this session to discover how a phone plan selection app, built with Flutter and Firebase, leverages Gemini 2.0 to enhance and simplify the customer experience. Gain insights into the technical architecture, identify actionable strategies to implement similar AI-driven solutions in your own apps, and understand the key principles of using AI to enhance the customer experience.

Organizations are racing to deploy generative AI solutions built on large language models (LLMs), but struggle with management, security, and scalability. Apigee is here to help. Join us to discover how the latest Apigee updates enable you to manage and scale gen AI at the enterprise level. Learn from Google’s own experience and our work with leading customers to address the challenges of productionizing gen AI.

Language models have already evolved to do much more than language tasks, principally in the domain of image, audio, and soon video. Join Mostafa Dehghani to explore the emergent frontier of multimodal generation, what Gemini’s world knowledge unlocks that domain specific models cannot create, and how developers should be thinking about AI as a next-generation creative partner.

Join us for an insightful discussion with Understood.org, a leading nonprofit dedicated to supporting the 70 million Americans with learning and thinking differences, and discover how empowering neurodiverse employees with tools like Google Workspace and Gemini can foster a more productive workplace.

Learn how Database Migration Service can help you modernize your SQL Server databases to unleash the power of cloud databases and open source PostgreSQL! Convert your SQL Server schema and T-SQL code to PostgreSQL dialect with a click of a button in the DMS Conversion Workspace. Some objects could not be fully converted? Gemini can suggest a fix. Not yet familiar with PostgreSQL features? Ask Gemini to teach you how to convert SQL Server features to PostgreSQL equivalent ones. While Gemini is there - ask it to optimize the converted code or add some comments to explain the business logic. Once your database is fully converted and optimized you can migrate the data with minimal downtime using the change data capture powered migration job and complete your migration journey.

AI is revolutionizing observability. Learn about Cloud SQL AI-powered Database Insights and how it can help you optimize your queries and boost database performance. We’ll dive deep into the new Insights capabilities for MySQL, PostgreSQL, and SQL Server, including the Gemini-powered chat agent. Learn how to troubleshoot those tricky database performance issues and get practical tips to improve the performance of your applications.

Gemini was built from the ground up to support our breakthrough long context window, with up to 2 million tokens in our largest models. Join Nikolay Savinov to explore how to get the most out of long context and what a world of infinite context might look like.

Join us for an interactive session where we’ll build, deploy, and scale inference apps. Imagine creating and launching generative AI apps that deliver personalized recommendations and stunning images, all with the unparalleled efficiency and scalability of serverless computing. You’ll learn how to build gen AI apps effortlessly using Gemini Code Assist; deploy gen AI apps in minutes on Cloud Run, using Vertex AI or on-demand, scale-to-zero serverless GPUs; and optimize the performance and cost of AI workloads by implementing best practices.

Learn how to evaluate and optimize the impact of AI-assisted software development with Gemini Code Assist. This session covers processes for measuring AI-assistance effectiveness, exploring quantitative and qualitative measures available with Gemini Code Assist, and integrating with Cloud Monitoring and Cloud Logging. Discover how to leverage DevOps Research and Assessment (DORA) metrics to track productivity gains. Whether you’re a developer, team lead, architect, or IT manager, you’ll gain insights into measuring the impact of AI assistance.

AI-enabled browser agents are in the news now, but it’s not always clear how they solve real-world problems. In this session, we’ll share our experience building a web browser agent by integrating Gemini into an end-to-end service that follows text instructions to take actions in a web application. We’ll take you through our journey of creating the agent, share the research that inspired us, and show how we’ve used the system to tackle practical problems like validating user flows in the UI and semantically checking web links.

This talk demonstrates a fashion app that leverages the power of AlloyDB, Google Cloud’s fully managed PostgreSQL-compatible database, to provide users with intelligent recommendations for matching outfits. User-uploaded data of their clothes triggers a styling insight on how to pair the outfit with matching real-time fashion advice. This is enabled through an intuitive contextual search (vector search) powered by AlloyDB and Google’s ScaNN index to deliver faster vector search results, low-latency querying, and response times. While we’re at it, we’ll showcase the power of the AlloyDB columnar engine on joins required by the application to generate style recommendations. To complete the experience, we’ll engage the Vertex AI Gemini API package from Spring and LangChain4j integrations for generative recommendations and a visual representation of the personalized style. This entire application is built on a Java Spring Boot framework and deployed serverlessly on Cloud Run, ensuring scalability and cost efficiency. This talk explores how these technologies work together to create a dynamic and engaging fashion experience.

Join Woosuk Kwon, Founder of vLLM, Robert Shaw, Director of Engineering at Red Hat, and Brittany Rockwell, Product Manager for vLLM on TPU, to learn about how vLLM is helping Google Cloud customers serve state-of-the-art models with high performance and ease of use across TPUs and GPUs.