Maven - End-to-End AI Engineering Bootcamp (3.2026)

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lesson1.mp4

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336 MB

code/Setting up your development environment..html

2.4 MB

code/Sprint 0 – Problem Framing, Infrastructure Setup & RAG Foundations/001 Understanding the AI product lifecycle.html

20 MB

code/Sprint 0 – Problem Framing, Infrastructure Setup & RAG Foundations/002 Tooling overview (LangGraph, vector DBs, LLM APIs, etc.).html

1.9 MB

code/Sprint 0 – Problem Framing, Infrastructure Setup & RAG Foundations/003 What is RAG.html

2.5 MB

code/Sprint 0 – Problem Framing, Infrastructure Setup & RAG Foundations/004 Embedding models & vector DB integration.html

1.1 MB

code/Sprint 0 – Problem Framing, Infrastructure Setup & RAG Foundations/005 Implementing basic observability foundations.html

1003 kB

code/Sprint 0 – Problem Framing, Infrastructure Setup & RAG Foundations/006 Evaluating basic end-to-end retrieval and generation.html

1.6 MB

code/Sprint 0 – Problem Framing, Infrastructure Setup & RAG Foundations/007 Hands-On Section.html

4.5 MB

code/Sprint 0 – Problem Framing, Infrastructure Setup & RAG Foundations/008 Amazon Electronics Category Dataset Overview.html

1.3 MB

code/Sprint 0 – Problem Framing, Infrastructure Setup & RAG Foundations/009 [OPTIONAL] AI Project Canvas.html

1.5 MB

code/Sprint 0 – Problem Framing, Infrastructure Setup & RAG Foundations/010 [OPTIONAL] Success metrics & evaluation frameworks.html

4.9 MB

code/Sprint 1 – Retrieval Quality & Context Engineering/001 RAG Data Ingestion Pipeline.html

5.3 MB

code/Sprint 1 – Retrieval Quality & Context Engineering/002 Pydantic and structured outputs.html

1.3 MB

code/Sprint 1 – Retrieval Quality & Context Engineering/003 Chunking strategies and Contextual embeddings.html

12 MB

code/Sprint 1 – Retrieval Quality & Context Engineering/004 Context Engineering and prompt management.html

1.3 MB

code/Sprint 1 – Retrieval Quality & Context Engineering/005 Re-Ranking and Hybrid Retrieval for better relevance.html

586 kB

code/Sprint 1 – Retrieval Quality & Context Engineering/006 (Optional) Automated prompt tuning.html

579 kB

code/Sprint 2 – Agents & Agentic Systems/001 Agent architecture and decision loops.html

1.8 MB

code/Sprint 2 – Agents & Agentic Systems/002 Tool use in agents.html

1.4 MB

code/Sprint 2 – Agents & Agentic Systems/003 Patterns for building Agentic Systems.html

1.1 MB

code/Sprint 2 – Agents & Agentic Systems/004 Memory in agent systems.html

2.1 MB

code/Sprint 2 – Agents & Agentic Systems/005 Reflection & Agent evaluation Frameworks.html

2.1 MB

code/Sprint 3 – Moving From Basic To Agentic RAG/001 Agent integrations with RAG systems.html

1.1 MB

code/Sprint 3 – Moving From Basic To Agentic RAG/002 Human Feedback and Fault Tolerance in Agentic Systems.html

595 kB

code/Sprint 3 – Moving From Basic To Agentic RAG/003 Human in the loop (HITL).html

598 kB

code/Sprint 3 – Moving From Basic To Agentic RAG/004 Model Context Protocol (MCP).html

1.2 MB

code/Sprint 4 – Multi-Agent Systems/01 Multi-agent systems and when to use it.html

594 kB

code/Sprint 4 – Multi-Agent Systems/02 Planning, delegation, and task routing among agents.html

1.3 MB

code/Sprint 4 – Multi-Agent Systems/03 Synchronization and memory sharing.html

589 kB

code/Sprint 4 – Multi-Agent Systems/04 Agent-to-agent communication protocols (A2A).html

1.3 MB

code/Sprint 5 – Deployment, Optimization and Reliability/01 Deployment architecture patterns for AI Systems.html

1.1 MB

code/Sprint 5 – Deployment, Optimization and Reliability/02 Managing latency and cost for AI applications.html

1.5 MB

code/Sprint 5 – Deployment, Optimization and Reliability/03 Securing AI systems.html

590 kB

code/Sprint 5 – Deployment, Optimization and Reliability/04 CI CD for AI applications.html

588 kB