LLM-Twin: Production-Ready AI Replica

An end-to-end production-ready LLM & RAG system.

From data gathering to productionizing LLMs using LLMOps good practices.

🤖 What is LLM-Twin

LLM-Twin is AI replica that writes social media posts or technical articles (like this one) using your own voice.

🪈 The architecture of the LLM Twin is split into 4 Python microservices

The data collection pipeline

• Crawl your digital data from various social media platforms, such as Medium, Substack and GitHub.
• Clean, normalize and load the data to a Mongo NoSQL DB through a series of ETL pipelines.
• Send database changes to a RabbitMQ queue using the CDC pattern.
• Learn to package the crawlers as AWS Lambda functions.

The feature pipeline

• Consume messages in real-time from a queue through a Bytewax streaming pipeline.
• Every message will be cleaned, chunked, embedded and loaded into a Qdrant vector DB.
• In the bonus series, we refactor the cleaning, chunking, and embedding logic using Superlinked, a specialized vector compute engine. We will also load and index the vectors to a Redis vector DB.

The training pipeline

• Create a custom instruction dataset based on your custom digital data to do SFT.
• Fine-tune an LLM using LoRA or QLoRA.
• Use Comet ML's experiment tracker to monitor the experiments.
• Evaluate the LLM using Opik
• Save and version the best model to the Hugging Face model registry.
• Run and automate the training pipeline using AWS SageMaker.

The inference pipeline

• Load the fine-tuned LLM from the Hugging Face model registry.
• Deploy the LLM as a scalable REST API using AWS SageMaker inference endpoints.
• Enhance the prompts using advanced RAG techniques.
• Monitor the prompts and LLM generated results using Opik
• In the bonus series, we refactor the advanced RAG layer to write more optimal queries using Superlinked.
• Wrap up everything with a Gradio UI (as seen below) where you can start playing around with the LLM Twin to generate content that follows your writing style.

Along the 4 microservices, you will learn to integrate 4 serverless tools:

• Comet ML as your experiment tracker and data registry;
• Qdrant as your vector DB;
• AWS SageMaker as your ML infrastructure;
• Opik as your prompt evaluation and monitoring tool.

🎓 Prerequisites

Category	Requirements
Skills	Basic understanding of Python and Machine Learning
Hardware	Any modern laptop/workstation will do the job, as the LLM fine-tuning and inference will be done on AWS SageMaker.
Level	Intermediate

🏗️ Project Structure

At Decoding ML we teach how to build production ML systems, thus the course follows the structure of a real-world Python project:

llm-twin-course/
├── src/                     # Source code for all the ML pipelines and services
│ ├── 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
├── .env.example             # Example environment variables template
├── Makefile                 # Commands to build and run the project
├── pyproject.toml           # Project dependencies

🚀 Install & Usage

To understand how to install and run the LLM Twin code end-to-end, go to the INSTALL_AND_USAGE dedicated document.

🥂 Acknowledgments

This project is based on the LLM Twin: Building Your Production-Ready AI Replica course by Decoding ML . Special thanks to the original authors and contributors for their invaluable course.

License

This course is an open-source project released under the MIT license. Thus, as long you distribute our LICENSE and acknowledge our work, you can safely clone or fork this project and use it as a source of inspiration for whatever you want (e.g., university projects, college degree projects, personal projects, etc.).# llm-twin