Published 1/2026
Created by K8s Point – Training House for GCP, AI & Kubernetes
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 62 Lectures ( 5h 46m ) | Size: 4 GB
All in 6 Hours – Practical Based Learning into Google Vertex AI, Code with Gemini, AutoML, Custom Training, AI Agents
What you’ll learn
✓ Learn Everything you need to know about Vertex AI with Practical Example
✓ Design, build, and deploy AI Agents using Google ADK and Agent Engine
✓ Understand Google’s AI model ecosystem (Gemini, Imagen, Veo, and more)
✓ Use Vertex AI Model Garden and Vertex AI Studio effectively
✓ Authenticate and access Google Models using API keys, service accounts, and ADC
✓ Learn How to access a Gemini Model from Python Code
✓ Work with Vertex AI notebooks, Colab Enterprise, and Jupyterlab 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
✓ Run agents locally and deploy them on Google Cloud
Requirements
● Very Basic of Python
● GCP Basics
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 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
Who this course is for
■ Anyone wants to Learn Vertex AI
■ All GCP Engineers – The Natural Next Step
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