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Build & Secure GenAl Applications with AWS Bedrock Bootcamp
Published 9/2026
Created by Yogesh Raheja
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 69 Lectures ( 6h 51m ) | Size: 3.8 GB
From Foundation Models to Production-Ready, Secure GenAI Applications
What you'll learn
Understand what Amazon Bedrock is and its key features and functionalities Navigate the Amazon Bedrock console and set up prerequisites using the AWS CLI Explore Foundation Models, test with Playgrounds, and configure model parameters and inference profiles Master the InvokeModel and Converse APIs using the Python SDK (Boto3) Analyze multimodal AI for image analysis and build your first chatbot Apply advanced prompting techniques like Zero-Shot, Few-Shot, Chain of Thought (CoT), ReAct, and Role-Based Prompting Understand tokens, tokenization, embeddings, and vector databases Build and query your own Knowledge Base utilizing RAG (Retrieval-Augmented Generation) Create low-code solutions using Amazon Bedrock Flows with conditions, do-while loops, iterators, and Lambda functions Secure your Generative AI applications by creating and applying Bedrock Guardrails Integrate the AgentCore Gateway into an interactive Python CLI and deploy to production Build an end-to-end Capstone Project: An HR Document Q&A System with a document processing pipeline and vector database integrationRequirements
No prior AI development experience needed Basic Python familiarity is helpful for the Boto3 SDK integrations A code editor (like VS Code) and terminal An AWS account to access the console and Bedrock featuresDescription
"This course contains the use of artificial intelligence."
Generative AI is transforming how we build software, and Amazon Bedrock provides a unified, secure platform to scale these applications using foundational models. In this course, you will learn Amazon Bedrock from the ground up, starting with an introduction to its key features, console navigation, and industry applications.
You will quickly move into hands-on territory, exploring available Foundation Models, testing them in the Playground, and managing model parameters. From there, you will make your first API calls using the InvokeModel and Converse APIs via the Python SDK (boto3), optimizing text generation, analyzing images, and building an end-to-end chatbot.
The course dives deep into prompt engineering, teaching you the anatomy of a prompt alongside advanced techniques like Zero-Shot, Few-Shot, Chain of Thought (CoT), Structured Output, and ReAct prompting. You will also explore tokenization, visual clustering for word embeddings, and Document Chunking to build and query your own Knowledge Base with semantic search.
Next, you will leverage Amazon Bedrock Flows for low-code development, implementing conditions, do-while loops, iterators, inline code, and Lambda functions. To ensure your applications are robust, you will master advanced functionalities like AgentCore integration, model fine-tuning, cost optimization, and implementing Bedrock's Security and Guardrails.
Everything culminates in a hands-on capstone project where you will build a complete HR Document Q&A System, featuring a document processing pipeline, vector database integration, and production deployment using AgentCore.
Course Structure
Concept-based lectures on Generative AI and Amazon Bedrock Hands-on Python SDK (boto3) and API demonstrations Deep dives into Prompt Engineering, Embeddings, and RAG Low-code workflow development using Bedrock Flows End-to-end HR Document Q&A Capstone Project with AgentCore deploymentCourse Contents
Introduction to Amazon Bedrock First Amazon Bedrock API call Bedrock Playground Model Parameters and Configurations InvokeModel API and Converse API Prompting Techniques and Prompt Management Embeddings and Vector Databases Document Chunking, Embeddings, and Semantic Search Knowledge Base and RAG No-Code platformusing Flows Flows using Builder, Prompt, KB, Conditions, Loops, Iterations, Inline & Lambda functions Getting Started with AgentCore Building the AgentCore Harness and Knowledge Gateway Bedrock's Security and Guardrails Model Customization and Fine Tuning Advanced Bedrock Functionalities End to End Capstone Project: Build HR Document Q & A SystemSpecial Note
This course is built around clarity and hands-on learning. Every technical concept is paired with a simple analogy so it sticks, and every prompting technique comes with a live demonstration so you see it working in real time. The end-to-end project at the end of the course are designed to bring together everything you have learned - so you leave with working agents, not just notes.
Who this course is for
Beginners who want a structured introduction to Generative AI on AWS Python and cloud developers looking to transition into AI application development Architects wanting to understand Foundation Models, RAG, and secure AI deployments Professionals who prefer practical, demo-driven learning utilizing the AWS console and SDKsHomepage
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