Generative AI can transform customer service, enhance employee productivity, automate business processes, and more. And by using Serverless, Google Cloud's “pay-as-you-go” compute platform to provide these experiences, you can focus on what's core to your business and leave the autoscaling to Google. In this talk, you'll learn how to run Google Cloud generative AI tools on Serverless, including how to use the Vertex AI Gemini API, how to use function calling to supplement a gen AI model with Serverless endpoints, and more.

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It can be challenging to know where and how to start building with Generative AI. Join this introductory workshop to learn about the tools and techniques needs to get started building with foundation models. We show you how to experiment with Gemini in the Cloud console and evaluate the performance of Gemini for your specific use case.

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Gemini is a family of generative AI models developed by Google DeepMind that is designed for multimodal use cases. The Gemini API gives you access to the Gemini Pro Vision and Gemini Pro models. In this spotlight lab, you will learn how to use the Vertex AI Gemini API with the Vertex AI SDK for Python to interact with the Gemini Pro (gemini-pro) model and the Gemini Pro Vision (gemini-pro-vision) model.

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At Google I/O last year, we announced several new AI Firebase Extensions using the PaLM API. This year, we’ve added support for Google's latest Gemini models. Easily add a chatbot, text summarizer, content generator, vector database pipeline, and more to your app without learning new APIs. In this session, get an end-to-end view of how you can use Firebase and Gemini to create an enterprise-ready customer support app. Build many apps with the powerful combo of Gemini's multimodal features and Firebase's convenient suite of developer tools.

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As machine learning (ML) systems continue to evolve, the ability to scale complex ML workloads becomes crucial. Scalability can be considered along two dimensions: expansive training of large language models (LLMs) and intricate distribution of reinforcement learning (RL) systems. Each has its own set of challenges, from computational demands of LLMs to complex synchronization in distributed RL.

This session explores the integration of Ray, Google Kubernetes Engine (GKE) and ML accelerators like tensor processing units (TPUs) as a powerful combination to develop advanced ML systems at scale. We discuss Ray and its scalable APIs, its mature integration with GKE and ML accelerators, and demonstrate how it has been used for LLMs and re-implementing the powerful RL algorithm, Muzero.

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A day in the life of a Google Cloud developer typically involves the use of multiple Google Cloud products and services. These products enable the developer to develop, test, deploy, and manage applications in the cloud. With assistance from Gemini, a developer can become more productive when using Google Cloud's products by using Gemini's interactive chat, code assistance, and embedded integrations. In this spotlight lab you will explore Gemini in an hands-on lab environment to see the different ways in which Gemini can be used in your development workflows.

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Coinbase is partnering with Google Cloud to propel their company to the forefront of AI innovation. Coinbase is poised to implement dozens of GenAI use cases this year, to augment the journeys of its employees and customers. As GenAI technology continues to evolve, Google's Model Garden offers Coinbase the flexibility to seamlessly integrate fundamental models like Gemini alongside third-party ones, empowering both their business and users. Join us to find out how VertexAI is helping Coinbase revolutionize the crypto industry, unlocking new possibilities and redefining the future of finance.

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Gemini is a contextual, real-time assistant baked right into Workspace — that lets you become a better writer, visual designer, data analyst, and project manager. In this session, discover how Gemini for Google Workspace empowers your teams to build more creative campaigns, maximize follow-through with prospects, and accelerate business opportunities. See real-world use cases showcasing how Gemini automates tasks, boosts efficiency, and frees time to focus on higher value work – driving greater impact, revenue, and ROI.

Please note: seating is limited and on a first-come, first served basis; standing areas are available

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Simplify and automate Application Programming Interface (API) development and integration with Gemini. Design and build APIs faster, connect any application, and reduce errors with AI-powered insights.

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Explore the orchestration of Google Cloud technologies behind the AI Penalty Challenge. Discover how Gemini's language capabilities on Vertex AI, Firestore's data management, Android's device integration, and the power of Google Cloud work in unison. Learn problem-solving strategies for building scalable, AI-powered experiences.

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Working with the agility and flexibility of a digital native or a big tech innovator can feel like an impossible mountain to climb for traditional enterprises. But empowering your teams with the right tools, like generative AI, can make this vision a reality. Join this session to learn how your organization can adopt a more collaborative mindset and begin to identify your road map to embracing generative AI. Hear from two customers who have embarked on their journey of a new way to work and glean insights from their lessons learned in adopting Gemini AI in Google Workspace.

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Explore your Looker data with natural language. This session dives into our open-source generative AI Looker extension, powered by Vertex AI large language models (LLM). Learn how to: - Ask questions using natural language: Explore data and gain insights intuitively - Deploy and manage: Understand the extension's architecture and set it up for your needs - Customize the extension: Change prompts if needed, give more examples, or fine-tune the LLM model for tailored results

Unleash the power of generative AI for data-driven decision making.

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Traditional infrastructure is no longer adequate for the exponentially growing demands of generative AI and LLMs. Join this session to learn how infrastructure design is meeting those demands, how organizations are adapting to capitalize on the new infrastructure landscape, and how this may evolve in future.

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In this workshop, you will learn how you can easily create a Retrieval Augmented Generation (RAG) application and how to use it. We will be highlighting AlloyDB Omni (our deploy-anywhere version of AlloyDB) with pgvector's vector search capabilities. You will learn to run an LLM and embedding model locally so that you can run this application anywhere. Creating an app in a secure way with LLMs playing around your data is harder than ever. Come and build with me!

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We’ve only just begun to realize the power of applying generative AI to security, but organizations using Gemini are already seeing improvement in how they manage complex security incidents. Learn how generative AI from Google Cloud can help seasoned security professionals quickly discover, investigate, and respond to threats, and help newcomers boost their skills to get more things done, faster. Join this session to learn about new features, hear from customers, and gain insights on how to address common security challenges with AI.

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Generative AI is rapidly growing in business and the popular imagination. Google Cloud was at the forefront of this revolution with the introduction of the Transformer architecture in 2017 and more recently, with the release of Gemini models. This session introduces JAX, a powerful framework and ecosystem for large model development, which we use to develop our Gemini models, and Keras - an easy to use higher level API for deep learning and gen AI.

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Learn about Wells Fargo’s journey to next-generation logging, with focus on the architecture that handles logging at scale for the third-largest U.S. Bank. We'll explore topics such as: architecture supporting infrastructure serving over 70 million customers; multicloud integration across on-premises, software as a service, and public cloud; programmatic and self-service migration of dashboards, alerts, and queries; logging infrastructure as code; sensitive data handling; Gemini accelerating troubleshooting and usability, and much more.

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AI is all the rage these days, but how can you make practical use of it without spending months of time learning this new technology? This session explains how to build an AI-powered content search tool for your own content in an afternoon, with a useful AI development pattern called retrieval augmented generation (RAG). We will demonstrate an updated version of the Docs Agent project that uses the Gemini API.

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Understanding the sensitivity of your most critical data assets allows you to unblock your workloads while reducing security, privacy, and compliance risk. In this session, you’ll learn how Sensitive Data Protection can help you gain awareness of your most critical sensitive data and how you can protect your application, AI, ML, and LLM workloads across the entire lifecycle from data to training to serving.

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Gemini Pro Vision supports multimodal prompts. You can include text, images, and video in your prompt requests and get text or code responses. This spotlight lab focuses on demonstrates a variety of multimodal use cases that Gemini can be used for. This list includes detecting objects in photos, understanding charts and diagrams, comparing images, generating a video description, extracting highlights/messaging of a video and other examples you will explore in a hands-on lab environment.

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