Agentic AI & RAG - Gen AI with AWS Bedrock, Google Vertex AI

Name: Agentic AI & RAG - Gen AI with AWS Bedrock, Google Vertex AI
Rating: 4.5 (60 reviews)

Generative AI with AWS Bedrock - S3, Lambda, API Gateway, Knowledge Base, Guardrails, Google Vertex AI - RAG, Agentic AI

Created bySoumen Kumar Mondal

Last updated 8/2025

English

English [Auto],

What you'll learn

Fundamentals of Generative AI – Understand the core concepts of Generative AI, including prompt engineering, cloud computing, and how AI models work.
AWS Bedrock – Learn how to set up AWS Bedrock, explore the console, and use services like S3, Lambda, and API Gateway to build AI-powered application
Knowledge Base and Retrieval-Augmented Generation (RAG) – Build applications that use RAG to interact with custom data stored in S3 using AWS Bedrock.
Agent Creation and Guardrails – Create powerful Agents with guardrails to ensure safe and reliable interactions with AI models.
Google Vertex AI – Explore Google’s Vertex AI offerings, learn how to set up Vertex AI Studio, and implement RAG and Agentic AI use cases.
Hands-On Projects – Implement real-world use cases, including building AI knowledge base applications, travel agent apps, and integrating external APIs and clou
Cloud Integration – Learn how to effectively integrate Generative AI with cloud services for scalability, security, and storage.

Course content

9 sections • 33 lectures • 5h 23m total length

Introduction of the course6:50
In this introduction section, we'll explore the exciting world of Generative AI and how it merges with powerful cloud platforms like AWS Bedrock and Google Vertex AI. Get ready to dive into hands-on projects that will equip you with the skills to build AI-driven applications!

Cloud Computing and Gen AI6:45
In this lecture, we'll explore the fundamentals of Cloud Computing and how it powers Generative AI applications. Learn how cloud platforms like AWS and Google Cloud provide the scalability, security, and tools needed to enhance AI solutions.
Fundamental of Generative AI (For beginners)8:53
In this lecture, we'll cover the fundamentals of Generative AI. This beginner-friendly introduction will set the stage for your journey into building powerful AI applications using cloud technologies.
Prompt Engineering (For beginners)10:33
In this lecture, we'll introduce you to the basics of Prompt Engineering, teaching you how to craft effective prompts to get the best responses from AI models.
Prompt Engineering Hands-on (For beginners)8:11
Will do hands on of prompt engineering on ChatGPT
Intro of Agentic AI (For beginngers)3:05
Basic idea and overview of agentic AI. Difference between non agentic and agentic AI flow.
Characteristics of Agentic AI (For beginneers)3:35
Few details of agentic AI.

Concept and features of Bedrock service18:58
In this lecture, we’ll dive into the concepts and features of AWS Bedrock, Amazon’s fully managed service for building and scaling Generative AI applications. You’ll learn about its powerful integration with pre-trained models, custom model support, and how it enables seamless interaction with AI technologies like LLMs for various use cases.
Bedrock - Account setup6:02
In this lecture, we'll walk you through the AWS Bedrock account setup, guiding you step-by-step on how to create and configure your AWS account for using Bedrock. Learn how to navigate the AWS Console and get everything ready to start building your Generative AI applications.
Bedrock - Console overview25:28
In this lecture, we'll provide an overview of the AWS Bedrock console, showing you how to navigate its interface and access key features. You’ll learn how to manage models, monitor usage, and explore the tools available for building and deploying your Generative AI applications.
Bedrock - LLM access and Playground6:47
In this lecture, we’ll guide you through accessing LLMs (Large Language Models) within AWS Bedrock and demonstrate how to use the Bedrock Playground for experimentation. You’ll learn how to interact with pre-trained models, fine-tune them, and explore their capabilities in a hands-on, user-friendly environment.

Overview of the use case6:40
Architecture overview2:26
Explore the knowledge base architecture, featuring a streamlit chatbot outside AWS, bedrock via boto3, retrieve and generate API, S3 data store, embedding, OpenSearch vector DB, and Lambda-based synchronization.
Setup Bedrock Knowledge Base4:57
Set up a bedrock knowledge base by uploading a document to an S3 bucket, linking the data source, selecting a text embedding model, and creating an OpenSearch vector store.
Sync Bedrock Knowledge Base7:51
Bedrock Boto3 environment setup - S3 integration4:59
Configure a python environment and boto3 to connect to S3, load AWS keys from a .env file with python-dotenv, and list buckets to verify the setup.
Implementation - Streamlit chatbot, knowledge base, s317:25
Implementation - Auto sync with Lambda6:54
Create a Lambda function to auto sync S3 data with a Bedrock knowledge base, triggered by S3 events, and start an ingestion job using knowledge base and data source IDs.

Google Gen AI offerings3:48
Explore Google Cloud gen AI offerings, including Vertex AI Studio, Bedrock, and Agent Builder, for low-code prompts, document summarization, and conversational AI with cloud storage, functions, and APIs.
Vertex AI - Features and pricing5:57
Account setup and Vertex AI landing page9:36
Vertex AI Studio - Features (Hands On)14:14
Explore Vertex AI Studio's features—prompt management, few-shot prompting, model selection, and safety filters—while uploading media, creating variable prompts, and generating outputs in text or JSON.
Use case: Vertex AI - Document search12:22
Use case: Travel agent AI assistant with agent builder (RAG)14:29
Build a travel agent with Vertex AI engine builder that uses data stores, cloud storage, and OpenAPI to answer city queries with RAG routing to third-party APIs.
Use case: Travel agent AI assistant with agent builder (External API)8:15
Build a travel agent AI using an OpenAPI specification and API layer to fetch real-time currency conversions via a convert currency endpoint, authenticated by an API key.
Use case: Travel agent AI assistant with agent builder (Cloud Function)12:24

Requirements

No programming or cloud experience required. A desire to learn about Generative AI, cloud services, and how they work together.

Description

This course is ideal for students, data scientists, AI/ML engineers, developers, and product managers who want to master Generative AI using AWS Bedrock and Google Vertex AI. No prior Python experience is required, making it accessible to beginners eager to dive into the world of AI without the steep learning curve.

We’ll cover the essentials of Generative AI and cloud computing before delving into hands-on projects using AWS Bedrock services like S3, Lambda, and API Gateway. You’ll build applications with knowledge base creation, RAG (Retrieval-Augmented Generation), and guardrail setups to ensure safe, reliable AI outputs.

In addition, you'll explore Google Vertex AI, where we’ll cover Agentic AI for dynamic, real-time decision-making and Vertex AI RAG to create intelligent AI systems. You’ll also integrate APIs and cloud functions to enhance your applications further.
We'll cover AWS AI offerings like Amazon Q, SageMaker AI and Google cloud AI offerings.

With comprehensive hands-on practice and clear explanations, this course ensures that you gain practical skills in Generative AI and cloud AI services. By the end, you’ll be equipped to build scalable, AI-powered applications, making it perfect for advancing your career in the ever-evolving AI field.

The course will get updated with new content regularly.

Who this course is for:

Beginners in AI and Cloud – Individuals with no prior experience in Python, AI, or cloud computing who want to gain practical skills in Generative AI.
Students – Those studying computer science, AI, or data science who want to explore hands-on AI and cloud applications.
Data Scientists and Machine Learning Engineers – Professionals looking to expand their expertise in Generative AI, RAG, and Agentic AI using AWS Bedrock and Google Vertex AI.
Developers – Software developers seeking to learn how to build AI-powered applications with cloud integration using AWS and Google Cloud.
Product Managers – Those managing AI-related products and looking to understand how Generative AI and cloud solutions can be used to improve product features.
Researchers – AI researchers interested in learning about the practical applications of Generative AI on cloud platforms and exploring new AI tools.

Agentic AI & RAG - Gen AI with AWS Bedrock, Google Vertex AI

What you'll learn

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Course content

Introduction1 lecture • 7min

Overview of Generative AI and Cloud6 lectures • 41min

Amazon AWS1 lecture • 15min

AWS - Bedrock4 lectures • 57min

AWS Use case: Generate Image through Bedrock , S3, Lambda4 lectures • 44min

AWS Usecase - Bedrock Knowledge Base or RAG application with S3, Boto3, Bedrock7 lectures • 51min

AWS Bedrock - Agentic AI and Guardrail2 lectures • 27min

Google Cloud - GenAI8 lectures • 1hr 21min

Quizzes0

Requirements

Description

Who this course is for: