The Ultimate Aws Bedrock & Generative Ai Bootcamp

Master AWS Bedrock, Foundation Models, RAG, Agents, and AI-driven automation. A hands-on guide to Generative AI Ops.

What you'll learn

Understand the fundamentals of Generative AI, Large Language Models (LLMs), and their impact across diverse tech roles.

Programmatically connect to AWS Bedrock using Python (Boto3) and invoke Foundation Models.

Configure Bedrock Agents and integrate them with AWS Lambda to perform real-world automated actions.

Securely manage IAM roles, permissions, and ARNs for safe agent–Lambda integration.

Build and query private knowledge bases on Bedrock using Amazon S3, Embedding Models, and OpenSearch.

Troubleshoot token usage, streaming responses, and cost management in Bedrock.

Requirements

Curiosity and willingness to experiment with cloud-based AI models and real-world use cases.

Description

In today’s rapidly evolving tech world, Generative AI is not just a buzzword—it’s a career-defining skill. From intelligent assistants to automated DevOps pipelines, Gen AI is transforming how we design, deploy, and manage modern systems. But learning where to start can feel overwhelming.This course is your step-by-step, practical guide to AWS Bedrock and Generative AI Ops. Whether you are a beginner or an experienced professional, you’ll learn how to harness the power of Foundation Models (FMs) like Anthropic’s Claude, Meta’s Llama, and Amazon’s Titan through Bedrock’s unified platform.We begin with the essentials—understanding AI, ML, Deep Learning, and LLMs. You’ll then dive into programmatic access with Python and Boto3, invoking models, analyzing outputs, and managing token-based pricing. Next, we explore Retrieval Augmented Generation (RAG) by creating knowledge bases from private data stored in Amazon S3, vectorizing it with embeddings, and storing it in OpenSearch.The course then advances into Agents and Lambda integration, teaching you how to build automated workflows that can send emails, manage AWS resources, or connect to APIs—all triggered by natural language prompts. Security, IAM permissions, and structured JSON responses are covered to ensure reliability and enterprise readiness.By the end, you’ll not just understand Generative AI—you’ll implement real-world AI Ops solutions that combine knowledge retrieval with automation, making you a valuable professional in the age of AI-driven technology.

Overview

Section 1: Introduction

Lecture 1 Generative AI: Beyond Prompt Engineering & Its Impact Across Tech Roles

Lecture 2 Exploring AWS Bedrock: Your Gateway to Next-Gen AI Models

Lecture 3 Foundation Models, Playground, and Key LLM Concepts

Lecture 4 How LLMs and Knowledge Bases Execute Tasks

Lecture 5 Interacting with AWS Bedrock from Python

Lecture 6 Advanced LLM Interaction: Running Models and Managing Outputs in Python

Lecture 7 Fine-Tuning LLM Responses: JSON Inputs and Inference Parameters

Lecture 8 Orchestrating Complex Gen AI Solutions with RAG and Agents

Lecture 9 Implementing RAG with AWS Bedrock Knowledge Bases

Lecture 10 Getting Answers from Your Proprietary Data with RAG

Lecture 11 Connecting Agents to APIs and External Services

Lecture 12 Creating Agents That Take Real-World Actions

Lecture 13 Securing Agents and Lambda in Bedrock

Developers, DevOps engineers, and cloud professionals looking to integrate Generative AI into their workflows.,Data analysts, data scientists, and AI enthusiasts interested in Retrieval Augmented Generation (RAG).,IT professionals and system administrators exploring automation with Gen AI Ops.,Students and beginners who want hands-on experience with AWS Bedrock and LLMs.,Technical leaders who want to understand the practical use cases of Bedrock for enterprise adoption.,Anyone curious about building real-world Gen AI applications with zero prerequisites.