Alexandr RichAlexandr Rich

Alexandr Rich · Build your foundations

Learning Roadmap

Master LLMs, retrieval, agents, evaluation, deployment and practical machine learning.

48 lessonsHosted video lessons9 resources
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The curriculum

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Introduction

  1. 01Program Structure (WATCH & READ FIRST)

LLMs & RAG

  1. 02LLM Fundamentals
  2. 03Prompt Engineering For Devs
  3. 04Working with LLM APIs
  4. 05Embeddings and Semantic Search
  5. 06Vector Databases
  6. 07Basic Retrieval Augmented Generation (RAG)
  7. 08Document Processing and Chunking
  8. 09Advanced Retrieval
  9. 10🏁 CHECKPOINT PROJECT

Integration, Agents & Orchestration

  1. 11Function Calling and Tool Use
  2. 12Structured Outputs and Validation
  3. 13LLM applications with frameworks
  4. 14Agent Fundamentals
  5. 15Agentic Design Patterns
  6. 16Orchestration with LangGraph
  7. 17Multi Agent Intro
  8. 18Multi-agent Systems
  9. 19Agent Memory and State
  10. 20Agentic RAG
  11. 21Model Context Protocol (MCP)
  12. 22🏁 CHECKPOINT PROJECT

Ops & Evaluation

  1. 23LLMOps Fundamentals
  2. 24Serving Open Source Models
  3. 25Deploying LLM Applications
  4. 26Observability and Monitoring
  5. 27CI/CD for LLM Applications
  6. 28Evaluation Fundamentals
  7. 29LLM-as-a-Judge
  8. 30RAG Evaluation
  9. 31Evaluating AI Agents
  10. 32Benchmarking Models
  11. 33🏁 CHECKPOINT PROJECT

Safety & Ethics

  1. 34Responsible AI Practices
  2. 35Data Ethics and Bias
  3. 36LLM Security and Risk
  4. 37Guardrails
  5. 38AI Governance and Frameworks
  6. 39🔑 Resource
  7. 40🏁 CHECKPOINT PROJECT

ML Foundations

  1. 41ML Fundamentals and Metrics
  2. 42scikit-learn: Pipelines and Model Training
  3. 43Data Engineering and Quality
  4. 44Experiment Tracking with MLflow
  5. 45Data Versioning with DVC
  6. 46Deployment and CI/CD
  7. 47Monitoring ML in Production
  8. 48🏁 CHECKPOINT PROJECT