llm-twin-course
decodingai-magazine/llm-twin-course
Learn to build an end-to-end production-ready LLM & RAG system using LLMOps best practices
Overview
This repository contains materials for a free course on building and deploying a production-grade AI system (LLM Twin), covering data collection, feature engineering, model training, deployment, and leveraging modern MLOps techniques.
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Install
pip install llm-twin-courseREADME
LLM Twin Course: Building Your Production-Ready AI Replica
Learn to architect and implement a production-ready LLM & RAG system by building your LLM Twin
From data gathering to productionizing LLMs using LLMOps good practices.
🎯 What you'll learn
By finishing the "LLM Twin: Building Your Production-Ready AI Replica" free course, you will learn how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices.
No more isolated scripts or Notebooks! Learn production ML by building and deploying an end-to-end production-grade LLM system.
📖 About this course
You will learn how to architect and build a real-world LLM system from start to finish - from data collection to deployment.
You will also learn to leverage MLOps best practices, such as experiment trackers, model registries, prompt monitoring, and versioning.
The end goal? Build and deploy your own LLM twin.
What is an LLM Twin? It is an AI character that learns to write like somebody by incorporating its style and personality into an LLM.
🪈 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 [Superlin