Speaker: Dr. Ali Arsanjani, Director of Applied AI Engineering; Head of AI Center of Excellence at Google Cloud

Speaker: Holt Skinner, Developer Advocate at Google Cloud AI

Live, expert-led sessions where you’ll build real agentic systems step-by-step.

J'ai changé d'avis sur les conversations à voix haute avec des robots. Ils sont devenus moins stupides, plus rapides (faible latence) et sont en fait très utiles pour la productivité au quotidien.

Nous savons tous comment interagir avec un LLM en envoyant un prompt en entrée et en recevant du texte et des images en sortie. Mais dans une session vocale live, où se termine le prompt et où commence la réponse ? Comment pouvons-nous maîtriser le flux pour agir ? Utilisons la fonctionnalité Multimodal Live du SDK Gemini Go pour créer une application.

Valentin est ingénieur à Google Cloud. Il s'intéresse à la performance, aux algorithmes, aux serveurs web, aux BDD, UX, DX, et au langage Go!

The data world has long been divided, with data engineers and data scientists working in silos. This fragmentation creates a long, difficult journey from raw data to machine learning models. We've unified these worlds through the Google Cloud and dbt partnership. In this session, we'll show you an end-to-end workflow that simplifies data to AI journey. The availability of dbt Cloud on Google Cloud Marketplace streamlines getting started, and its integration with BigQuery's new Apache Iceberg tables creates an open foundation. We'll also highlight how BigQuery DataFrames' integration with dbt Python models lets you perform complex data science at scale, all within a single, streamlined process. Join us to learn how to build a unified data and AI platform with dbt on Google Cloud.

Découvrez comment Bouygues Telecom réinvente le parcours de vente grâce à l'IA. En intégrant les technologies Gemini et Agent Builder au cœur de leur SI, ByTel offre une expérience client enrichie et personnalisée à grande échelle pour l'ensemle des profils clients.

Construisez des agents business avec une vrai logique métier dans l'écosysteme Google Cloud. On vous montrera comment un agent peut se déployer à l'échelle et peut interagir avec des progiciels des bases de données, voir même data lake de votre SI !

The Generative AI revolution is here, but so is the operational headache. For years, teams have matured their MLOps practices for traditional models, but the rapid adoption of LLMs has introduced a parallel, often chaotic, world of LLMOps. This results in fragmented toolchains, duplicated effort, and a state of "Ops Overload" that slows down innovation.

This session directly confronts this challenge. We will demonstrate how a unified platform like Google Cloud's Vertex AI can tame this complexity by providing a single control plane for the entire AI lifecycle.

Discover how to build a powerful AI Lakehouse and unified data fabric natively on Google Cloud. Leverage BigQuery's serverless scale and robust analytics capabilities as the core, seamlessly integrating open data formats with Apache Iceberg and efficient processing using managed Spark environments like Dataproc. Explore the essential components of this modern data environment, including data architecture best practices, robust integration strategies, high data quality assurance, and efficient metadata management with Google Cloud Data Catalog. Learn how Google Cloud's comprehensive ecosystem accelerates advanced analytics, preparing your data for sophisticated machine learning initiatives and enabling direct connection to services like Vertex AI. 

Are you ready to build the next generation of data-driven applications? This session demystifies the world of Autonomous Agents, explaining what they are and why they are the future of AI. We’ll dive into Google Cloud's comprehensive platform for creating and deploying these agents, from our multimodal data handling to the seamless integration of Gemini models. You will learn the principles behind building your own custom data agents and understand why Google Cloud provides the definitive platform for this innovation. Join us to gain the knowledge and tools needed to architect and deploy intelligent, self-sufficient data solutions.

The growth of connected data has made graph databases essential, yet organisations often face a dilemma: choosing between an operational graph for real-time queries or an analytical engine for large-scale processing. This division leads to data silos and complex ETL pipelines, hindering the seamless integration of real-time insights with deep analytics and the ability to ground AI models in factual, enterprise-specific knowledge. Google Cloud aims to solve this with a unified "Graph Fabric," introducing Spanner Graph, which extends Spanner with native support for the ISO standard Graph Query Language (GQL). This session will cover how Google Cloud has developed a Unified Graph Solution with BigQuery and Spanner graphs to serve a full spectrum of graph needs from operational to analytical.

Data leaders sharing the good, the bad and the ugly about governing data across company boundaries and even outside of the company walls. Key topics to unlock are Data Governance, Data Products, AI for Data

Discover how Google Cloud's AI-native platform is transforming data science, moving beyond traditional methods to empower you with an intuitive experience, an open ecosystem, and the ability to build intelligent, data-native AI agents. This shift eliminates integration headaches and scales your impact, enabling you to innovate faster and drive real-world outcomes. Explore how these advancements unify your workflows and unlock unprecedented possibilities for real-time, agent-driven insights.

A2A protocol and agent-to-agent communication; theory and live coding.

A2A Protocol

Episode 5 – A2A Protocol.

Episode 4 – Deploy AI Agents to Production.

Deploy AI Agents to Production

Deploy AI agents to production on Google Cloud Platform; theory and live coding.

Foundations with ADK

MCP & Tool Integration