AWS Bedrock and Agentic AI
Published 6/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 17h 35m | Size: 28.28 GB

Build Production-Ready Generative AI Apps on AWS

What you'll learn
Learn AWS Bedrock Concepts and Functionality using the Console
Learn about Large Language Models in Bedrock (.i.e LLMs), How to use, When to use and Expections.
Learn how to use Building Client Applications and SDKs for Bedrock Agents using LLMs
Using Claude Code to build Bedrock Applications
How to use API Keys
Learn how to compare LLMs
Use the Bedrock Playground, Watermark Detection and Tokenizers
How to Infer a Model using Profiles, Batch, Provised Throughput and OnDemand
Creating Custom Models - Importing , Reinforcement Learning,, Fine Tuning, and Distillation
Buildig Agents with ActionGroups and Knowledge Bases.
Building Agents - Automatic Reasoning, Data Automation, Flows, Guadrails, Knowledge Bases and Prompt Management.
How to use Evaluations of Custom LLM
Setting up Logging for Bedrock LLM Access.
Vector Embeddings

Requirements
AWS Basic Account, IAM, Python and Claude Code in VSCode

Description
Master Amazon Web Services' fully managed generative AI service with this comprehensive, hands-on course designed for developers, architects, and cloud professionals. AWS Bedrock gives you direct access to powerful foundation models from leading AI providers - including Anthropic's Claude, Meta's Llama, Mistral, and Amazon's own Titan models - all without managing any infrastructure.

In this course, you'll start from the ground up, learning what AWS Bedrock is, how it fits into the broader AWS ecosystem, and why enterprises are adopting it for production AI workloads. From there, you'll dive into the core APIs: invoking models, streaming responses, and comparing outputs across different foundation models to choose the right one for your use case.

You'll build real-world applications using Bedrock Agents - autonomous AI assistants that can reason, plan, and call your own APIs and Lambda functions. You'll implement Retrieval-Augmented Generation (RAG) with Bedrock Knowledge Bases, connecting your own documents and data sources to foundation models for accurate, grounded responses.

The course covers security best practices, including IAM permissions, VPC endpoints, and data privacy guarantees. You'll also explore Bedrock Guardrails to filter harmful content and enforce compliance policies in enterprise deployments.

By the end, you'll have built multiple production-ready AI applications and have the confidence to architect scalable, secure generative AI solutions on AWS. Whether you're new to generative AI or migrating existing workloads to AWS, this course gives you everything you need to succeed with Amazon Bedrock.

Who this course is for
Developers that want to Build Agentic AI Applications using in Large Scale Production Environments

Homepage

https://www.udemy.com/course/aws-bedrock-and-agentic-ai

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