Description

A fast, hands-on guide for developers to build Generative AI, RAG pipelines, and AI Agents using Google Vertex AI.Google Vertex AI has become the backbone for building production-grade Generative AI applications using Gemini models, vector search, RAG engines, and agentic workflows.This crash course is designed to take you from zero to building real GenAI systems on Vertex AI. In around 6 hours, you’ll gain practical, working knowledge of:Gemini and Google’s model ecosystemGoogle GenAI SDK with Python CodeVertex AI Studio and Model GardenNotebooks and development environmentsCore ML concepts on Vertex AIRetrieval-Augmented Generation (RAG)AI Agents using Google ADK and Agent EngineMulti Agent System with ADKColab EnterpriseJupyterlab Workbench Train with AutoMLCustom TrainingDeploy with Vertex AI EndpointsModel RegistryBy the end of this course, you will be able to:Understand Google’s AI model ecosystem (Gemini, Imagen, Veo, and more)Use Vertex AI Model Garden and Vertex AI Studio effectivelyAuthenticate and access models using API keys, service accounts, and ADCWork with Vertex AI notebooks, Colab Enterprise, and WorkbenchUnderstand Vertex AI training options including AutoML and custom trainingBuild, deploy, and test ML models on Vertex AICreate embeddings and vector indexes for semantic searchImplement RAG pipelines using RAG Engine and Vertex AI SearchDesign, build, and deploy AI Agents using Google ADK and Agent EngineRun agents locally and deploy them on Google Cloud