Aws Bedrock Masterclass Rag, Guardrails & Enterprise Ai

[Emperor2011]

Aws Bedrock Masterclass: Rag, Guardrails & Enterprise Ai
Published 6/2026
Created by Suryansh Gupta
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 8 Lectures ( 49m ) | Size: 990.7 MB​

Build, secure & deploy RAG applications using Amazon Bedrock Knowledge Bases, Guardrails, Lambda, and enterprise AI
What you'll learn
Build a RAG question-answering app using Amazon Bedrock Knowledge Bases, Retrieve API, and RetrieveAndGenerate API with real company data.
Implement Bedrock Guardrails to filter harmful content, block PII, deny off-topic queries, and meet enterprise compliance requirements.
Secure Gen AI apps using IAM least-privilege, VPC endpoints, CloudWatch logging, and CloudTrail auditing on AWS.
Deploy AI applications on AWS Lambda and API Gateway with streaming responses, cost optimization, and provisioned throughput strategies.
Design multi-turn conversation patterns using Amazon Nova Lite and understand zero-shot vs context-aware prompting for enterprise Q&A.
Explore Bedrock Agents for building autonomous, multi-step AI workflows
Understand chunking strategies, vector embeddings, and semantic search inside Bedrock
Requirements
Basic Python skills and AWS fundamentals (IAM, S3, Lambda). An AWS account with Bedrock access enabled. No prior AI or ML experience needed.
Description
Generative AI is no longer a research topic - it's a production requirement. But building AI applications that are accurate, secure, and compliant on AWS requires more than calling an API. This course gives you every layer of that stack.
In this masterclass, you will go from the fundamentals of Amazon Bedrock to deploying a fully secured, enterprise-grade AI chatbot with Retrieval-Augmented Generation (RAG) and content guardrails. Every concept is taught through hands-on labs taken directly from advanced AWS practitioner training.
You will learn why zero-shot prompting breaks down at enterprise scale - and exactly how RAG fixes it by grounding AI responses in real company knowledge. You will implement Amazon Bedrock Knowledge Bases, call the Retrieve and RetrieveAndGenerate APIs in Python, and build a streaming Q&A system that answers questions about real documents.
Then you will harden that system with Bedrock Guardrails: blocking off-topic content, filtering PII, and ensuring your app meets organizational compliance requirements.
Security is built in throughout. You will configure IAM roles with least-privilege access, set up VPC endpoints for private Bedrock access, and instrument CloudWatch and CloudTrail logging - because in enterprise environments, auditability is non-negotiable.
The course closes with production deployment: Lambda and API Gateway integration, provisioned throughput decisions, cost optimization strategies, and an introduction to Bedrock Agents for autonomous AI workflows.
By the end, you will have a deployable portfolio project and the confidence to build Gen AI systems your organization can trust.
Who this course is for
Cloud developers, solutions architects, and ML engineers who want to build, secure, and deploy production-grade generative AI applications on AWS using Amazon Bedrock.
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