Amazon Bedrock : Generative AI, AI Agents, MCP, EVALs, RAG

About this course

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Unlock the Power of Amazon Bedrock to Build AI-Powered Applications

Welcome to Mastering Amazon Bedrock, a comprehensive course designed to help you harness the power of AWS Bedrock’s tools and services. Whether you're a beginner or an experienced developer, this course will take you step-by-step through concepts, configurations, and hands-on exercises that showcase the potential of AWS Bedrock in building intelligent applications.

What You’ll Learn:

Knowledge Bases (KB): Dive deep into the concept of vector embeddings and retrieval-augmented generation (RAG), essential for optimizing large-scale AI applications. Learn how to configure Knowledge Bases and integrate them seamlessly with other AWS Bedrock tools using practical examples to solidify your understanding.

RAG with Amazon Bedrock - We will use Anthropic Claude Model with OpenSearch Serverless as vector storage to perform the RAG operationsRAG with Open Source - We will also use OpenAI's ChatGPT model with in memory vector storage to perform RAG operations

Retrievers - RAG pattern relies heavily on retrieval. There are many ways to retrieve data for summarization. We will learn and explore about different ways to retrieve the contents. Followed by a hands-on activity AI Agents: Master the configuration of AWS Bedrock agents to streamline AI workflows. Gain hands-on experience in implementing action groups, handling parameters, and orchestrating requests effectively to Knowledge Bases. Understand how agents serve as the backbone of dynamic and intelligent AI interactions. We will cover 2 use cases of AI Agents. Multimodal Nutritional AI Agent - We

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