
Discover the Services in Bedrock
Vector Embeddings explained in detail.
Compare nova micro, nova pro, and nova lite to assess how tokens, latency, and cost affect output. Explore tuning options like temperature and guardrails for text, image, and video tasks.
Explore the AWS Bedrock tokenizer to estimate input and output tokens, manage cost and quotas, and compare models to optimize prompts, with driveway paving price examples.
Claude Code builds a simple client Python application that calls bedrock LLM using a cross-region inference with 3 regions.
Explore distillation in Bedrock by training a smaller student model from a larger teacher model, using invocation logs or generated data, and ensure model compatibility and proper data processing.
Build and validate a distilled model, then deploy an on-demand model. Compare Nova Pro and Nova Micro to balance speed and cost.
Demonstrate supervised fine-tuning in Bedrock, covering Bedrock-style JSONL data, schema, prompts, data validation, training with epochs and learning rate, and comparing to distillation.
Demonstrate building a master Bedrock agent that delegates gutter queries to a gutter agent using a knowledge base and action group, with a React frontend and Claude code integration.
Explore building a Bedrock knowledge base with a vector store on S3, embedding PDFs, and enabling an agent to retrieve and generate answers with document references.
Explore bedrock data automation for video analysis, generating transcripts, audio transcripts, and video summaries with taxonomy and chapters, and learn to export results to S3 and DynamoDB.
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.