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.

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As generative AI applications mature, retrieval-augmented generation (RAG) has become popular for improving large language model-based apps. We expect teams to move beyond basic RAG to autonomous agents and generative loops. We'll set up a Weaviate vector database on Google Kubernetes Engine (GKE) and Gemini to showcase generative feedback loops.

After this session, a Google Cloud GKE user should be able to: - Deploy Weaviate open source on GKE - Set up a pipeline to ingest data from the Cloud Storage bucket - Query, RAG, and enhance the responses

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Unleash learning's revolution with generative AI! This session equips you to upskill curriculums and reskill teams. We'll explore generative AI's potential, best practices for integration, and tools to track progress and measure impact. Identify skill gaps, develop reskilling plans, and empower developers with new skills. Leave ready to transform your programs.

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Companies often possess vast amounts of untapped data. This lightning talk demonstrates how AI can transform this data into actionable insights. Learn how to integrate Firebase and Firestore Vector Search to create a powerful AI-driven knowledge base, capable of directly answering user questions based on your Firestore data.

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Explore the capabilities of RoostGPT in our upcoming session, an innovative testing platform at the forefront of generative AI and Large Language Models (LLMs). RoostGPT is engineered to redefine test case generation, enabling the creation of detailed and accurate test suites without the need for manual effort. It facilitates the automated generation of Unit, API, Integration, and Functional tests, significantly increasing productivity and expanding test coverage. 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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In this session, we’ll explore the art of writing effective, multi-turn AI prompts and how Gemini, our most capable AI model, can help you achieve more than you ever thought possible. We’ll cover how large language models (LLMs) work and how outputs are based on the quality of an input. We’ll share practical tips on what makes great prompts and best practices for drafting prompts in Gemini, Gmail, Docs, Sheets, and Slides. You’ll leave this session empowered to prompt like a pro.

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Have you ever wondered what it takes to build a unique multicustomer generative AI experience? How would you secure it? Scale it? Where would you start? This is the story of how our ambitious two-pizza team built an interactive custom LLM-powered chatbot in only two weeks. We'll cover what we learned, what we'd do differently, and what’s changed.

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Join this session to learn how Gemini in BigQuery can help you accelerate time to insights by enhancing productivity, and optimizing the cost and performance of data workloads.

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Paramount+ currently relies on manual processes to create the essential metadata and video summaries used across its platform. These elements are crucial for showcasing content and creating personalized experiences for viewers. Join us as we explore how the Paramount+ team harnessed the power of VertexAI Text Bison to streamline this process. Discover their approach using prompt engineering best practices and the impressive results achieved by automating these key content tasks.

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Empower your organization with the future of app development: no-code, AI-powered solutions built using Google AppSheet and Google Cloud AI. Business users will discover the ease of automating processes while IT leaders will learn to empower their teams and accelerate time-to-value through powerful AI integrations. Join us to discover how these innovative tools can transform your organization, enabling everyone to drive remarkable results and collaborate effectively across devices and data sources.

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Cultivating a belonging: Community through Cloud-based AI and Ancestral Intelligence. This session explores how artificial intelligence (AI), infused with emotional intelligence (EQ) and fueled by "ancestral intelligence" (the other AI), archived in the cloud, can create belonging. The cloud's capacity to store memories, emotions, and nostalgia can help us create our own AI legacy for future generations.

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