Build RAG on Google Cloud Using Vertex AI Course in Tampa

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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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 – 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.

what will you get

Key Features & Highlights

Key Features 1 Feature Title #1 (Optional) Enter key feature title Feature Description #1 Enter key feature description

Learn to build and deploy AI applications using Vertex AI (Gemini) with guided hands-on labs.

End-to-End RAG Pipeline Development

Build complete Retrieval-Augmented Generation pipelines from data ingestion to response generation.

Vector Search & Embeddings

Implement semantic search using embeddings and Vertex AI Vector Search for accurate retrieval.

BigQuery Integration for AI

Combine structured enterprise data with AI responses using BigQuery integration.

This course delivers a complete, hands-on learning experience focused on building production-ready RAG (Retrieval-Augmented Generation) applications on Google Cloud. It combines foundational concepts with real-world implementation using Vertex AI, Vector Search, BigQuery, and Cloud Run. Learners gain practical exposure to designing end-to-end AI pipelines, implementing secure cloud architectures, and deploying scalable AI applications aligned with enterprise standards. Through guided labs, real-world scenarios, and a capstone project, the course ensures strong technical understanding and job-ready skills in Generative AI and cloud engineering.

Why do Build RAG on Google Cloud Using Vertex AI at Nevolearn

Experiential Learning

Learn like never before. Not just learning, you interact and gain real experience.

Get practical, real-world learning experience
Engage in interactive sessions and activities
Apply concepts through hands-on exercises

Skill Development

Build skills that matter. Go beyond theory and develop job-ready expertise.

Track and measure your skill progress
Identify strengths and areas for improvement
Gain industry-relevant knowledge and tools

Career Success

Achieve your career goals with structured learning and expert guidance.

Learn from industry experts and mentors
Prepare for certifications and real-world challenges
Boost your career growth with in-demand skills

Build RAG on Google Cloud Using Vertex AI Curriculum

Understand cloud computing basics, Google Cloud Platform (GCP) services, and the fundamentals of Generative AI. Learn what Retrieval-Augmented Generation (RAG) is and why it is important for building accurate AI applications.

Learn IAM concepts including users, roles, permissions, and service accounts. Implement secure access control for cloud resources.

Understand how to store and manage documents for AI applications using Google Cloud Storage and organize enterprise data effectively.

Learn how large language models work, why hallucinations occur, and how grounding improves response accuracy in AI systems.

Explore the full RAG pipeline including ingestion, chunking, embeddings, vector storage, retrieval, and response generation.

About Build RAG on Google Cloud Using Vertex AI Certification

Follow these simple steps to earn your professional certification and validate your expertise.

Enroll in the Google Cloud RAG training program

Step 1

Complete all 20 structured modules

Step 2

Participate in hands-on labs and exercises

Step 3

Build and implement RAG pipeline components

Step 4

Develop and deploy a capstone AI application on Google Cloud

Step 5

Enroll in the Google Cloud RAG training program

Step 1

Complete all 20 structured modules

Step 2

Participate in hands-on labs and exercises

Step 3

Build and implement RAG pipeline components

Step 4

Develop and deploy a capstone AI application on Google Cloud

Step 5

Submit final project (RAG assistant) with evaluation results

Step 6

Successfully complete course assessment

Step 7

Receive official course completion certification

🎓 Certification

Prerequisites

Basic computer literacy (files, browser usage, internet)
Interest in Generative AI, cloud computing, and automation
No advanced programming experience required
Basic understanding of cloud concepts is helpful but not mandatory

Who should attend the Build RAG on Google Cloud Using Vertex AI training

This course is ideal for beginners and professionals who want to build expertise in Generative AI and cloud-based application development. It is suitable for freshers, developers, cloud engineers, analysts, and IT professionals looking to transition into AI engineering roles. Even learners from non-technical backgrounds can follow the structured, beginner-friendly approach and gain practical skills in building AI applications on Google Cloud.

Generative AI Developer

Cloud AI Engineer

RAG Engineer

AI Application Developer

Data / Business Analyst

Cloud Engineer

AI Solutions Engineer

COMMON QUESTIONS

Build RAG on Google Cloud Using Vertex AI FAQs

Freshers, developers, cloud engineers, analysts, and IT professionals interested in Generative AI and Google Cloud can enroll.

No, advanced coding skills are not required. The course is designed to be beginner-friendly.

Basic cloud knowledge is helpful but not mandatory, as the course starts from fundamentals.

Yes, the course is designed to support learners from both technical and non-technical backgrounds.

Skills Covered

What Will You Learn?

Building end-to-end RAG pipelines on Google Cloud

Vertex AI (Gemini) usage and deployment

Vector Search and embedding workflows

BigQuery integration for AI applications

Cloud Run deployment and scaling

AI app development and architecture design

IAM and cloud security best practices

Evaluation, monitoring, and optimization of AI systems

Soaring Demand and Accelerated Growth

Generative AI Engineer

Annual Salary

Workers/Salary

₹6L

Entry Level

₹12L

Mid Level

₹25L

Experienced

Hiring Companies

Build and deploy AI applications using RAG pipelines, LLMs, and cloud platforms like Google Cloud and Vertex AI.

FOR Generative AI Engineer

Annual Salary

Workers/Salary

₹6L

Entry Level

₹12L

Mid Level

₹25L

Experienced

Hiring Companies

Build and deploy AI applications using RAG pipelines, LLMs, and cloud platforms like Google Cloud and Vertex AI.

for Generative AI Engineer

Generative AI Engineers design intelligent systems that generate accurate, context-aware responses by combining AI models with enterprise data sources.

Set your teams up with this course

Group Discount Available

This Google Cloud RAG course equips learners with practical, job-ready skills to build enterprise-grade Generative AI applications. Through hands-on labs and real-world project implementation, participants gain deep expertise in designing, deploying, and managing Retrieval-Augmented Generation systems using Google Cloud services. The course not only strengthens technical knowledge but also enhances career opportunities in the rapidly growing AI and cloud computing industry.

Top Companies Rely on Us to Upskill Their Workforce

BENEFITS

Transforming your team

Build production-ready RAG applications on Google Cloud

Gain hands-on experience with Vertex AI (Gemini) and Vector Search

Create portfolio-ready projects for job interviews

Earn an industry-recognized GCP AI certification

CERTIFICATION

Earn a certificate on completion of this course

After finishing Nevolearn's Build RAG on Google Cloud Using Vertex AI course, you'll earn an industry-recognized professional certificate. This certificate is designed for sharing on LinkedIn, allowing you to highlight your accomplishments and share your new skills with your network.

Validating your expertise with a professional certification helps you stand out in the job market and provides tangible proof of your commitment to continuous learning and professional growth.

Build RAG on Google Cloud Using Vertex AI Certificate

TESTIMONIALS

What Learners are Saying

4.8/5•3,666 Reviews

Got questions?

Still have a question? Get in Touch with our Experts

This Google Cloud RAG course is designed to provide practical Generative AI training for building real-world applications using RAG on GCP. Learners gain hands-on experience with Vertex AI training, working with Gemini AI models to create intelligent, context-aware systems powered by enterprise data.

The course focuses on building an end-to-end RAG pipeline, including document ingestion, embedding generation, and retrieval using Vector Search. You will also integrate structured data with BigQuery AI and design scalable architectures for modern AI app development.

Through guided labs and real-world scenarios, students learn Cloud Run deployment to launch secure, production-ready AI applications. This cloud AI course emphasizes best practices in security, monitoring, and performance optimization, making it ideal for enterprise environments.

As a comprehensive AI engineering course, it prepares learners for advanced roles in enterprise AI training and cloud-based intelligent systems. Upon completion, participants earn a recognized GCP AI certification, validating their expertise in building scalable, enterprise-grade AI solutions on Google Cloud.

Expand your expertise in Generative AI and cloud-based application development with our advanced Google Cloud RAG course. This program helps learners build production-ready AI systems using Vertex AI (Gemini), Vector Search, BigQuery, and Cloud Run for scalable deployment.

Designed for beginners and professionals, this cloud AI training focuses on creating Retrieval-Augmented Generation (RAG) pipelines that combine document ingestion, embedding generation, vector indexing, and grounded response generation. You will gain hands-on experience working with enterprise-grade tools like Cloud Storage, Document AI, and IAM security.

To further strengthen your learning, explore related paths such as Cloud Computing Fundamentals, AI/ML Engineering, and Data Engineering on GCP. These complementary programs enhance your ability to build, secure, and optimize cloud-based AI applications across industries.

Recommended Learning Path:
Cloud Basics → Google Cloud Fundamentals → RAG Systems → Advanced AI/ML Engineering

By following this structured learning path, you can build strong expertise in AI application development, improve career opportunities in cloud AI roles, and become proficient in designing enterprise-ready intelligent systems.