A Python-based open-source course, Decoding AI's LLM Twin Course, providing src modules and a guided install usage doc for building a production-ready LLM and RAG system. The repository includes a four-microservice architecture and references to AWS SageMaker, Qdrant, Bytewax, Comet, and Opik.
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What it is
LLM Twin Course: Building Your Production-Ready AI Replica describes a course to architect and implement a production-ready LLM twin system, from data collection to deployment, using MLOps practices.
How it works
The project is structured into four Python microservices:
- data_crawling: Data collection pipeline code
- data_cdc: Change Data Capture (CDC) pipeline code
- feature_pipeline: Feature engineering pipeline code
- training_pipeline: Training pipeline code
- inference_pipeline: Inference service code
- bonus_superlinked_rag: Bonus RAG optimization code It demonstrates end-to-end pipelines: data crawling, real-time feature streaming, vector embedding into Qdrant, LLM fine-tuning (LoRA/QLoRA), experiment tracking (Comet ML), evaluation, and deployment via AWS SageMaker.
Project structure details:
llm-twin-course/
├── src/
│ ├── data_crawling/
│ ├── data_cdc/
│ ├── feature_pipeline/
│ ├── training_pipeline/
│ ├── inference_pipeline/
│ └── bonus_superlinked_rag/
├── .env.example
├── Makefile
├── pyproject.toml
Getting started
Install & Usage: to understand how to install and run the code end-to-end, go to the INSTALL_AND_USAGE document. The README notes that you can run everything using that dedicated document, and recommends reading the articles for full understanding.
The README provides a direct reference: "> Check the INSTALL_AND_USAGE doc for a step-by-step installation and usage guide."
Recent releases
- latest 0: none
Traction
Stars: 4380 Forks: 732 Open issues: 8
Caveats
License: MIT Created: 2024-03-08 Last push: 2026-04-20
Behind the repo
Not applicable in the provided facts