Google Vertex AI Crash Course -Gen AI, Gemini, AI/ML, Agents

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 ecosystem

• Google GenAI SDK with Python Code

• Vertex AI Studio and Model Garden

• Notebooks and development environments

• Core ML concepts on Vertex AI

• Retrieval-Augmented Generation (RAG)

• AI Agents using Google ADK and Agent Engine

• Multi Agent System with ADK

• Colab Enterprise

• Jupyterlab Workbench

• Train with AutoML

• Custom Training

• Deploy with Vertex AI Endpoints

• Model Registry

By 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 effectively

• Authenticate and access models using API keys, service accounts, and ADC

• Work with Vertex AI notebooks, Colab Enterprise, and Workbench

• Understand Vertex AI training options including AutoML and custom training

• Build, deploy, and test ML models on Vertex AI

• Create embeddings and vector indexes for semantic search

• Implement RAG pipelines using RAG Engine and Vertex AI Search

• Design, build, and deploy AI Agents using Google ADK and Agent Engine

• Run agents locally and deploy them on Google Cloud