Build a multimodal search engine with Gemini and Vertex AI. This hands-on lab demonstrates Retrieval Augmented Generation (RAG) to query documents containing text and images. Learn to extract metadata, generate embeddings, and search using text or image queries.

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!

Startup developers, are you ready to build? In this hands-on workshop, you'll design and code a generative AI app using Gemini and Vertex AI. Through focused coding exercises, you'll master essential skills in prompt engineering, multimodal AI, and real-world implementation. Walk away with functional AI code—and the foundation to power your AI-driven future.

**Please bring your laptop to get the most out of this hands-on session**

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!

AI is revolutionizing the startup landscape, unlocking new possibilities for innovation, efficiency, and growth. Join industry leaders as they share bold predictions, critical challenges, and practical strategies to help you harness AI for your business in 2025 and beyond.

Unlock the Power of Fine-Tuning with Apps Script! Learn how to optimize pre-trained models for specific tasks using Google Apps Script. This session covers exporting data from Sheets to Cloud Storage as JSONL, building an Apps Script prompt explainer backend, and creating service accounts for secure access to Vertex AI and Cloud Storage. We'll also show how to collect, transform, and split data for training, launch the fine-tuning process, and test results in Vertex AI and a Google Chat bot. Master fine-tuning for practical AI applications.

Retrieval Augmented Generation (RAG) is a powerful technique to provide real time, domain-specific context to the LLM to improve accuracy of responses. RAG doesn't require the addition of sensitive data to the model, but still requires application developers to address security and privacy of user and company data. In this session, you will learn about security implications of RAG workloads and how to architect your applications to handle user identity and to control data access.

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We are bringing Google’s research and innovations in artificial intelligence (AI) directly to your data in BigQuery. Join this session to learn about BigQuery’s built-in ML capabilities, such as model inferences, and how to use Gemini, Google's most capable and flexbile AI model yet, directly within BigQuery to simplify advanced use cases such as sentiment analysis, entity extraction, and many more.

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According to a 2023 Stack Overflow survey, the combination of Firebase and Google Cloud is the #1 choice for learning how to develop apps. Join this session to learn why this is true and how you can best leverage Firebase and Google Cloud together. Whether you’re a startup or a world’s leading retailer, you can easily get started with Firebase, and grow your business with Google Cloud. In this session, hosted by product leaders from both teams, they’ll discuss across products including Firebase, Vertex AI, and Cloud Run.

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Continuous Deployment can be a roadblock in the MLOps lifecycle, often requiring custom pipelines and complex configurations. Solution? The new integrations of Google Cloud Deploy and Vertex AI revolutionizes machine learning (ML) deployment by automating the entire process, and makes it easy to roll back through idempotent releases. The groundbreaking integration of Cloud Deploy and Vertex AI lets you test, validate, and deploy your ML models in minutes, without writing a single line of code.

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This session demonstrates how to use large language models (LLMs) to translate ideas directly into cloud architecture blueprints. You’ll learn how to generate designs from these blueprints with natural language processing. We’ll also use an existing LLM model specialized in code generation to understand our language dialect to generate cloud architecture diagrams. Finally, we'll also show you a web app on Google Cloud that allows users to interact with the model and use the generated artifacts in practice.

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Don’t miss the brilliance of Gemini in a real world live demo that showcases two examples of the mind blowing accuracy and power (and fun!) of AI. Witness a food and wine pairing plus a style session that is sure to be Next’s most stylish and delish live demo! By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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Businesses need to predict what customers want and create personalized experiences to gain a competitive advantage and drive revenue. They need to deliver customized, tailored interactions that increase customer acquisition, improve loyalty and increase satisfaction. Join Fullstory’s Head of Data Products to learn how Data + Engineering teams can supercharge tools like DialogFlow and BigQuery with unprecedented behavioral data to accurately forecast and create experiences that outpace the competition and keep customers coming back for more. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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Traditional enterprises can unlock their full potential and achieve transformative results by adopting a Cloud-first, AI-first approach. In this session, Infosys experts share a comprehensive blueprint powered by Infosys Topaz and Infosys Cobalt offerings on Google Cloud. Gain insights into the power of this dynamic duo and learn how Infosys is uniquely positioned to navigate enterprises through this transformative journey. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

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Join us to learn how to activate the full potential of your data with AI in BigQuery. Take an in-depth look at how BigQuery's core integration with generative AI models like Gemini, coupled with its petabyte-scale analytics capabilities, enables new possibilities for gaining insights from your data. Learn how to derive insights from your untapped and unstructured data such as images, documents, and audio files, and explore BigQuery vector search and multi-modal embeddings, all powered by Google's industry-leading AI capabilities in BigQuery using simple Cloud SQL queries. You will also learn how Unilever is creating a data strategy that allows data teams to scale efficiently and rapidly experiment with AI models and gen AI use cases.

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Come to this fireside chat with Seth Vargo to learn more about the ultimate hybrid cloud use case. We'll explore use cases where Alphabet products run on some of your favorite Google Cloud offerings such as Google Kubernetes Engine (GKE). Why don't we run everything at Alphabet on Google Cloud? Why do some products run partially on cloud? How do Alphabet engineers take advantage of products like GKE, Cloud Run, Vertex AI, and Gemini exposed over hybrid channels? We'll shed light on how our internal innovation influences the products available to our customers and vice-versa.

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In an industry that has been hit by change over the last two decades, media companies were forced to do two things: adapt or die. In this session, you will learn how US News and World Report, founded as a media company in 1933, chose to put their customers first and implemented Vertex AI Search. After launching the feature for only a few weeks, US News saw a double digit impact in key metrics like click-through rate, time spent on page, and traffic volume to its pages. Join us to learn how US News pulled it off with Google Cloud!

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In this session, Gemini leads will cover the tips, tricks, insights and lessons learned from helping over 30-plus teams at Google Cloud build world-class developer productivity tools using generative AI. Session highlights include: the "secret sauce" behind GenAI Chat for both IDE and Cloud Console experiences; hidden “gotchas” behind code completion and generation experiences for over 21 languages; how developer teams have to change their processes when adding nondeterministic technology to their stack, and much more.

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BigQuery allows you to generate multimodal embeddings and perform vector searches directly on your data without complex preprocessing steps. Simplify the process of finding relevant data, identifying patterns and trends, and clustering similar objects together.

Learn how to generate embeddings using familiar BigQuery SQL syntax with multimodal inputs (text, images, audio). We’ll then review how to use BigQuery’s vector search capabilities to explore data in new and innovative ways, leading to faster decision-making and improved insights.

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Discover how approachable and versatile building custom generative AI solutions for Google Workspace can be with Google Apps Script. In this session, we‘ll explore how developers are creating innovative integrations between Gemini‘s powerful large language models and Google Workspace. Get real-world examples of how AI can enhance Google Workspace functionality, and learn about the potential of custom solutions to address unique needs. Get ready to be inspired – the evolution of leveraging AI within Google Workspace is just beginning.

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The emergence of foundation models and generative AI has introduced a new era for building AI systems. Selecting the right model from a range of architectures and sizes, curating data, engineering optimal prompts, tuning models for specific tasks, grounding model outputs in real-world data, optimizing hardware – these are just a few of the novel challenges that large models introduce. Delve into the fundamental tenets of MLOps, the necessary adaptations required for generative AI, and capabilities within Vertex AI to support this new workflow.

Click the blue “Learn more” button above to tap into special offers designed to help you implement what you are learning at Google Cloud Next 25.