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Agentic AI & RAG - AWS Bedrock, Google Vertex AI, S3, Lambda

Soumen Kumar Mondal

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.

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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.

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What's inside

Learning objectives

  • 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.

Syllabus

Introduction

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!

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Overview of Generative AI and Cloud

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.

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.

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.

Will do hands on of prompt engineering on ChatGPT

Basic idea and overview of agentic AI. Difference between non agentic and agentic AI flow.

Few details of agentic AI.

Amazon AWS

In this lecture, we’ll explore the AWS Generative AI Services- Amazon Q, Amazon Bedrock, Sagemaker AI, AI infra.

AWS - Bedrock

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.

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.

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.

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.

AWS Use case: Generate Image through Bedrock , S3, Lambda

Use case architecture including Bedrock, S3, API gateway, Lambda

Lambda code to integrate with Boto3 and S3.

Bedrock Implementation - Lambda, Boto3 and S3 (continue)
Bedrock Implementation - Pre signed URL and API Gateway.
AWS Usecase - Bedrock Knowledge Base or RAG application with S3, Boto3, Bedrock
Overview of the use case
Architecture overview
Setup Bedrock Knowledge Base
Sync Bedrock Knowledge Base
Bedrock Boto3 environment setup - S3 integration
Implementation - Streamlit chatbot, knowledge base, s3
Implementation - Auto sync with Lambda
AWS Bedrock - Agentic AI and Guardrail
Bedrock Agent - create and test
Bedrock Guardrail
Google Cloud - GenAI
Google Gen AI offerings
Vertex AI - Features and pricing
Account setup and Vertex AI landing page
Vertex AI Studio - Features (Hands On)
Use case: Vertex AI - Document search
Use case: Travel agent AI assistant with agent builder (RAG)
Use case: Travel agent AI assistant with agent builder (External API)
Use case: Travel agent AI assistant with agent builder (Cloud Function)
Quizzes

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Covers AWS AI offerings like Amazon Q and SageMaker AI, alongside Google Cloud AI offerings, providing a broad understanding of available tools
Includes hands-on projects using AWS Bedrock services like S3, Lambda, and API Gateway, which are essential for practical application of skills
Explores Google Vertex AI, covering Agentic AI for dynamic decision-making and Vertex AI RAG for intelligent systems, which are cutting-edge topics
Teaches knowledge base creation, RAG (Retrieval-Augmented Generation), and guardrail setups, which are crucial for ensuring safe and reliable AI outputs
Requires learners to set up accounts on AWS and Google Cloud, which may involve providing payment information even with free tiers
Features integration of APIs and cloud functions to enhance applications, which is essential for building scalable, AI-powered solutions

Save this course

Save Agentic AI & RAG - AWS Bedrock, Google Vertex AI, S3, Lambda to your list so you can find it easily later:
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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Agentic AI & RAG - AWS Bedrock, Google Vertex AI, S3, Lambda with these activities:
Review Cloud Computing Fundamentals
Solidify your understanding of cloud computing concepts to better grasp the infrastructure supporting Generative AI applications in AWS Bedrock and Google Vertex AI.
Browse courses on Cloud Computing
Show steps
  • Review the basics of cloud computing models (IaaS, PaaS, SaaS).
  • Understand the differences between AWS and Google Cloud.
  • Familiarize yourself with key cloud services like compute, storage, and networking.
Brush Up on Python Basics
Practice basic Python syntax and data structures to prepare for coding examples and hands-on projects using Boto3 and Streamlit.
Browse courses on Python
Show steps
  • Review Python syntax, data types, and control flow.
  • Practice writing simple Python scripts.
  • Familiarize yourself with common Python libraries.
Read 'Generative AI with Python and TensorFlow 2'
Gain a deeper understanding of generative AI models and techniques to enhance your ability to work with AWS Bedrock and Google Vertex AI.
View Generative AI with Python and TensorFlow 2 on Amazon
Show steps
  • Read the chapters related to GANs and VAEs.
  • Experiment with the code examples provided in the book.
  • Relate the concepts to the models used in AWS Bedrock and Google Vertex AI.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a Simple Chatbot with AWS Lex
Practice building a chatbot using AWS Lex to understand conversational AI concepts and prepare for more advanced agentic AI applications.
Show steps
  • Design the chatbot's conversation flow and intents.
  • Implement the chatbot using AWS Lex.
  • Test and refine the chatbot's responses.
Write a Blog Post on RAG Applications
Solidify your understanding of Retrieval-Augmented Generation (RAG) by explaining the concept and its applications in a blog post.
Show steps
  • Research RAG and its use cases.
  • Outline the key points for your blog post.
  • Write and publish the blog post.
Create a Presentation on Agentic AI
Deepen your knowledge of Agentic AI by creating a presentation that explains its concepts, benefits, and use cases.
Show steps
  • Research Agentic AI and its applications.
  • Design the presentation slides with clear explanations and visuals.
  • Practice delivering the presentation.
Read 'LangChain in Action'
Explore LangChain as an alternative framework for building LLM-powered applications and compare its features with AWS Bedrock and Google Vertex AI.
View Melania on Amazon
Show steps
  • Read the chapters related to prompt engineering and agent creation.
  • Experiment with the code examples provided in the book.
  • Compare LangChain's approach with the methods taught in the course.

Career center

Learners who complete Agentic AI & RAG - AWS Bedrock, Google Vertex AI, S3, Lambda will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Agentic AI & RAG - AWS Bedrock, Google Vertex AI, S3, Lambda.
Cover image
Think and Grow Rich - English : For Personal Growth...
Save
Provides a practical guide to building applications with LangChain, a powerful framework for developing LLM-powered applications. It covers various topics, including prompt engineering, data augmentation, and agent creation. While the course focuses on AWS Bedrock and Google Vertex AI, LangChain offers a complementary approach to building AI applications. It is valuable as additional reading to expand your toolkit and explore different development paradigms.
Melania
Hardcover
$$
From Here to the Great Unknown: Oprah's Book Club:...
Hardcover
$$
War
Hardcover
$$
The Message
Hardcover
$$
Good Energy: The Surprising Connection Between...
Hardcover
$$
Be Ready When the Luck Happens: A Memoir
Hardcover
$$
The Anxious Generation: How the Great Rewiring of...
Hardcover
$$
Onyx Storm (Deluxe Limited Edition) (The Empyrean,...
Hardcover
$$
Spooky Cutie: Coloring Book for Adults and Teens...
Paperback
$
The Leaf Thief: (The Perfect Fall Book for Children...
Paperback
$$
Cover image
Generative AI with Python and TensorFlow 2
Save
Provides a comprehensive guide to generative AI models using Python and TensorFlow 2. It covers various techniques, including GANs, VAEs, and transformers, offering practical examples and code implementations. While the course focuses on AWS Bedrock and Google Vertex AI, this book offers a broader understanding of the underlying models. It is valuable as additional reading to deepen your knowledge of generative AI.
Generative AI with Python and TensorFlow 2
Paperback
Check
Generative AI with Python and TensorFlow 2
Kindle Edition
Check

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Effort
5.5 total hours
Via
Udemy
Instructor
Soumen Kumar Mondal
Language
English
Good to know
Covers AWS AI offerings like Amazon Q and SageMaker AI, alongside Google Cloud AI offerings, providing a broad understanding of available tools
Includes hands-on projects using AWS Bedrock services like S3, Lambda, and API Gateway, which are essential for practical application of skills
Explores Google Vertex AI, covering Agentic AI for dynamic decision-making and Vertex AI RAG for intelligent systems, which are cutting-edge topics
Teaches knowledge base creation, RAG (Retrieval-Augmented Generation), and guardrail setups, which are crucial for ensuring safe and reliable AI outputs
Requires learners to set up accounts on AWS and Google Cloud, which may involve providing payment information even with free tiers
Features integration of APIs and cloud functions to enhance applications, which is essential for building scalable, AI-powered solutions
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