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

  • Understand the fundamentals of AWS Bedrock and its builder tools.
  • Configure Knowledge Bases and use vector embeddings for intelligent solutions.
  • Design and manage Agents with Action Groups and Knowledge Base integration.
  • Implement Prompt Management with variables and dynamic flows.
  • Orchestrate advanced workflows using Flows, Agents, and Prompts.

Course Content

21 sections • 85 lectures • 8h 5m total length

4.9 (2 reviews)

FREE

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8 hours on-demand video
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Certificate of completion
  • 10,000 foot view on Language Models
  • LLM Inference Parameters
  • Semantic Search with Vector Embedding
  • RAG – Retrieval Augmentated Generation
  • User Role and Policy Setup
  • KB with IAM User
  • KB Configuration
  • KB Hands-On
  • Resource Clean Up
  • RAG pipeline with Open Source Framework
  • Advanced Topic – Retrievers Deep Dive
  • Inroduction to AI Agents
  • Agentic Design with Runtime
  • Agents Configuration Walkthrough
  • Hands-On Agents with Action Group
  • Hands-On Action Group with Parameter
  • Hands-On Agents with KnowledgeBase
  • Agents Orchestration (aka Planner Agent) – Deep Dive
    Resource Clean Up
  • Agentic Use Case with Multimodal, Multi-Hop and ReAct Architecture
  • ReACT Prompt for AI Agents
  • Understand the Planner Agent
  • Multimodal Agentic Use Case with Multiple Tools
  • Introduction to CrewAI
  • Install CrewAI
  • Define Agents and Tasks
  • Travel Agent Base Classes
  • Planner Agent with Crewbase
  • Multi Agent Execution with Crewbase
  • Evaluate Multi Agentic Execution
  • Introduction to Penetration Testing
  • Hands On Penetration Test using ZapProxy Tool
  • Cybersecurity/Penetration Testing AI Agent
  • Penetration Test using Tools Call
  • Run Cybersecurity Penetration Test with AI Agent
  • Run and Evaluate Cybersecurity Penetration Test with AI Agent
  • Introduction to Prompt
  • Hands-On Prompt Management
  • Hands-On Variables in Prompt Management
  • Introduction to Flows
  • Flows – Hands-On
  • Resource Clean Up
  • Lab – Build Multi-Agentic use case using Flows, Agents, Prompt and KB
  • Introduction to Prompt Injection
  • Introduction to Hallucination
  • Detect Hallucination
  • Guardrails Components
  • Hands-On Guardrails
  • Multimodal-Image Guardrails
  • Prompt Guard Model – Theory
  • Hands On Prompt Guard Model
  • Hands On – Detect Hallucination with Phi3 Hallucination Judge
  • Types of Evals
  • Evaluators Runtime
  • Retriever and Generate (RAG) – Evaluator
  • Model as a Judge – Evaluator
  • RAG Evaluator
  • Faithfulness (aka Hallucination) Evaluator
  • SAS Evaluator, ContextRelevance Evaluator
  • Hands On Batch Inference
  • Foundation Model – Fine tune model
  • Resource Clean up
  • Evolution of MCP – Current Solutions and their Limitations
  • Client Server Architecture
  • MCP Architecture
  • MCP Server Components
  • MCP Transport Types
  • MCP Communication
  • MCP Flow Diagram – Server, Client and Host communication over Transport Layer
  • Install Dependencies with UV package
  • Walkthrough Weather API
  • Invoke Weather API
  • Getting MCP Server Ready
  • MCP Host, Client and Server
  • MCP Inspector
  • Amazon Bedrock InlineAgent – Intro
  • Amazon Bedrock Agent Console
  • Inline Agent vs Bedrock Agent
  • Inline Agent Class Walkthrough
  • AWS Profile – CLI
  • IAM Access Key
  • Bedrock Agent with Time MCP Server
  • Bedrock Agent with Perplexity MCP Server
  • Cost Analysis Agent – Multi MCP Servers and Builder Tools
  • Cost Analysis Agent – Evaluate Results

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