Enterprise Generative AI Systems on AWS Certification Course

Design, Secure, Scale, and Govern Production-Ready Generative AI, RAG, Agents, and Multimodal Systems on AWS

Sub Category

• Other IT & Software

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Objectives

• Design complete, production-ready enterprise Generative AI architectures on AWS from user channels through models, data, security, and operations.
• Build Generative AI applications using Amazon Bedrock, Amazon Nova, Anthropic Claude, Meta Llama, Mistral, and other foundation models.
• Create secure Retrieval-Augmented Generation systems using Bedrock Knowledge Bases, Amazon OpenSearch Serverless, S3 Vectors, Aurora PostgreSQL and GraphRAG.
• Design and build enterprise AI agents using Bedrock Agents, AgentCore, Strands Agents SDK, LangChain, LangGraph, Step Functions, and Bedrock Flows.
• Connect Generative AI systems to enterprise data stored in Amazon S3, Aurora, RDS, DynamoDB, Redshift, SaaS applications, internal APIs, and on-premises systems
• Build scalable application and API layers using Route 53, CloudFront, AWS WAF, API Gateway, Lambda, ECS, Fargate, App Runner, and EKS.
• Create batch, streaming, and event-driven data-ingestion pipelines using AWS Glue, AppFlow, DataSync, EventBridge, SQS, Kinesis, Lambda, and Step Functions.
• Process, chunk, enrich, embed, and index documents, images, audio, video, tables, forms, and other multimodal enterprise content.
• Protect AI applications against prompt injection, unsafe outputs, data exposure, unauthorized tool execution, hallucinations, and malicious retrieved content.
• Implement enterprise identity, authorization, governance, monitoring, evaluation, DevOps, cost management, audit logging, backup, and disaster recovery controls

Pre Requisites

1. No previous Generative AI or machine-learning experience is required.
2. A basic understanding of cloud computing concepts will be helpful but is not mandatory.
3. Familiarity with AWS services is useful, although all major architecture components are explained during the course.
4. Basic programming or API knowledge may help with hands-on activities, but the architecture lessons are suitable for non-developers.
5. An AWS account is recommended for students who want to complete the practical labs.
6. Access to a modern computer, internet connection, and web browser is required.
7. Students should be comfortable learning through architecture diagrams, service comparisons, practical scenarios, and hands-on exercises.
8. A willingness to explore security, governance, data, application, and operational considerations across the complete AI lifecycle is the most important prerequisite.

FAQ

• Q. How long do I have access to the course materials?
  • A. You can view and review the lecture materials indefinitely, like an on-demand channel.
• Q. Can I take my courses with me wherever I go?
  • A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!

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