decodingai-magazine/llm-twin-course resource

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4385 stars · 731 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A free hands-on course (source code plus 12 lessons) that teaches how to build an end-to-end production-ready LLM and RAG system by creating an 'LLM Twin' that writes in your style. It covers the full lifecycle including data crawling, ETL pipelines, streaming feature pipelines, fine-tuning, and deployment using LLMOps best practices.

Use cases

• learn to build a production-ready RAG system
• how to fine-tune an LLM on my own writing style
• course on LLMOps and MLOps best practices
• build an end-to-end LLM system with microservices
• learn data pipelines for LLM training with vector databases
• deploy LLM applications on AWS with infrastructure as code
• hands-on lessons for streaming ETL with Bytewax and Qdrant

When to choose

• you want a free, structured, project-based curriculum for production LLM engineering
• you learn best by building a complete system rather than isolated notebooks
• you want exposure to real MLOps tooling like experiment trackers, model registries, and prompt monitoring

When to avoid

• you need a ready-to-use production product rather than educational code
• you want a quick tutorial instead of a 12-lesson course commitment
• you are looking for a lightweight library to drop into an existing project

Facets

learning-resource · maturity active

rag llm-training etl streaming machine-learning web-scraping vector-database infrastructure-as-code large-language-models machine-learning artificial-intelligence tutorials cloud-computing python cloud llmops mlops course hands-on-lessons microservices qdrant rabbitmq bytewax aws-lambda fine-tuning production-ml retrieval-augmented-generation data-engineering docker

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