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Build RAG on Google Cloud Using Vertex AI

Master building enterprise-grade AI applications with this hands-on Google Cloud RAG course. Learn to design, develop, and deploy Retrieval-Augmented Generation (RAG) systems using Vertex AI (Gemini), Vector Search, BigQuery, and Cloud Run. Gain real-world experience in creating secure, scalable AI assistants that retrieve accurate insights from enterprise data. Perfect for beginners and professionals aiming to build production-ready generative AI solutions and advance their careers in cloud AI and automation.

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Users enrolled+1,200 Enrolled
Build real-world RAG applications on Google Cloud using Vertex AI and Vector Search
Learn end-to-end RAG pipeline development from document ingestion to deployment
Hands-on training with Gemini AI, BigQuery, and Cloud Run
Enterprise training for teams:
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Some of the highlights of this course.

60 Hours Total Training Hours

20 Modules Total Modules

20+ Labs Hands-On Labs

1 End-to-End RAG Project Capstone Projects

8+ Services Cloud Tools Covered

800+ Professionals Students Enrolled

This course is designed to provide measurable, hands-on learning outcomes through structured modules and real-world labs. With extensive practical exposure to Google Cloud services, learners build and deploy a complete RAG application, gaining the skills required for enterprise-level AI development and cloud engineering roles.

Course Description

Build RAG Applications on Google Cloud โ€“ Course Overview Overview

This Google Cloud RAG course is a comprehensive, hands-on training program designed to help learners build end-to-end Retrieval-Augmented Generation (RAG) applications using Google Cloud Platform. The course covers key technologies including Vertex AI (Gemini), Vector Search, BigQuery, Cloud Storage, and Cloud Run, enabling learners to design, develop, and deploy enterprise-ready AI systems. Through a structured learning approach, participants gain practical experience in document ingestion, embedding generation, retrieval workflows, and grounded response generation. By the end of the program, students will be able to build secure, scalable AI assistants and deploy them in real-world cloud environments with confidence.