GenAI Expertise on AWS
Generative AI on AWS for Professionals: Enterprise-grade Gen AI, RAG, and Agentic AI with Amazon Bedrock, Knowledge Bases, Agents, AgentCore, and Guardrails.
4.7
(720 students)
8 Weeks · 80 hours
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What is this course about?
Generative AI on AWS for Professionals
Enterprise-Grade Generative AI, RAG, and Agentic AI with Amazon Bedrock
Build production-ready Generative AI systems on AWS—from foundation models and Bedrock APIs through RAG, agents, guardrails, and enterprise deployment.
Who This Program Is For:
Software Engineers, Cloud Engineers, Solution Architects, Data Engineers, AI/ML Engineers, Technical Managers, DevOps Engineers, and IT professionals building Gen AI on AWS.
What You'll Master:
- Bedrock foundations — FM selection, APIs, streaming, IAM, and enterprise architecture patterns
- Prompt engineering — Zero/few-shot, chain-of-thought, ReAct, governance, and injection prevention
- RAG on AWS — Embeddings, Knowledge Bases, hybrid search, and enterprise-scale retrieval
- Production architectures — Serverless, event-driven, API Gateway, and multi-model systems
- Open source + Bedrock — LangChain, LlamaIndex, and hybrid pipelines
- Agentic AI — Strands agents, Bedrock Agents, AgentCore, and Amazon Q Developer
- Security & ops — Guardrails, observability, evaluation, cost optimization, and GRC
Hands-On Labs in Every Module
Labs cover the Bedrock console, API development, RAG with Knowledge Bases, enterprise assistants, agent workflows, AgentCore, guardrails, and end-to-end deployment.
Capstone — Enterprise Agentic AI Assistant on AWS
Build a production-grade platform: documents → chunking → embeddings → Knowledge Base → RAG → agentic reasoning → tool calling → guardrails → monitoring → production deployment.
Learning Outcomes — You Will Be Able To:
- Build enterprise applications with Amazon Bedrock • Engineer prompts for business workflows • Implement RAG with Bedrock Knowledge Bases • Integrate LangChain/LlamaIndex with Bedrock • Build agentic workflows and Bedrock Agents/AgentCore • Implement guardrails and governance • Monitor, optimize, and evaluate Gen AI on AWS • Deploy production-ready enterprise AI solutions
How Deepskilling Makes You Industry-Ready:
🚀 AWS-native depth: Bedrock-first curriculum aligned to how enterprises deploy Gen AI today.
💼 Portfolio capstone: End-to-end agentic assistant architecture you can present in interviews.
🏆 What Sets Us Apart:
✓ Ten modules with labs in every module
✓ Bedrock Agents, AgentCore, Knowledge Bases, and Guardrails
✓ Professional cohort (intermediate–advanced) with instructor-reviewed milestones
Course Features
Post Graduate Diploma
8 Weeks of Content
Hands-on Projects
Community Support
Lifetime Access
Student Reviews
Amit Sharma
Bedrock API and Knowledge Base labs were production-realistic. My capstone enterprise assistant cleared security review—hired as AWS Generative AI Engineer at ₹22 LPA.
Tech MahindraPriya Nair
Module 8 on Bedrock Agents and AgentCore finally connected the dots between RAG and autonomous workflows. Portfolio impressed every AWS partner interview.
Wipro TechnologiesVikram Choudhary
Guardrails and observability modules matched what our bank needed for regulated Gen AI. Promoted to lead our internal Bedrock platform squad.
Infosys LimitedKavya Desai
LangChain + Bedrock hybrid lab saved weeks on our knowledge assistant POC. Serverless architecture patterns were immediately reusable.
CognizantSanjay Reddy
Streaming API and multi-turn conversation labs built confidence before we migrated chatbots to Bedrock. Clear IAM guidance throughout.
Capgemini IndiaMeera Pillai
RAG evaluation and cost optimization workshops paid for themselves on our first Bedrock invoice review. Got multiple offers as Enterprise RAG Engineer.
HCL TechnologiesThomas Anderson
AgentCore lab was the differentiator—our team now ships agentic features on a standard pattern. Strong enterprise deployment focus.
AWS Partner NetworkEmily Chen
End-to-end capstone architecture (documents → KB → RAG → agents → guardrails) is now our reference template for customer proposals.
Cloud Consulting FirmDaniel Kim
Amazon Q Developer integration tips accelerated our agent build cycle. Responsible AI section was sharper than internal enablement decks.
APAC Systems Integrator
