
Discover the Services in Bedrock
Vector Embeddings explained in detail.
Claude Code builds a simple client Python application that calls bedrock LLM using a cross-region inference with 3 regions.
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.