--- title: "llm-twin-course" type: "tool" slug: "decodingai-magazine-llm-twin-course" canonical_url: "https://www.graphcanon.com/tools/decodingai-magazine-llm-twin-course" github_url: "https://github.com/decodingai-magazine/llm-twin-course" homepage_url: null stars: 4368 forks: 733 primary_language: "Python" license: "MIT" categories: ["inference-serving", "developer-tools", "data-retrieval", "model-training"] tags: ["llmops", "bytewax", "comet-ml", "docker", "large-language-models", "aws", "generative-ai", "machine-learning-engineering"] updated_at: "2026-07-07T19:58:56.858311+00:00" --- # llm-twin-course > Build a production-ready LLM & RAG system using LLMOps best practices This repository contains materials for a comprehensive course that teaches users how to design, train, and deploy an end-to-end production-grade Large Language Model (LLM) system. The curriculum spans data gathering, cleaning, normalization, feature extraction into vector databases like Qdrant, and the final production deployment using modern MLOps practices. ## Facts - Repository: https://github.com/decodingai-magazine/llm-twin-course - Stars: 4,368 · Forks: 733 · Open issues: 8 · Watchers: 76 - Primary language: Python - License: MIT - Last pushed: 2026-04-20T10:53:45+00:00 ## Categories - [Inference & Serving](/categories/inference-serving.md) - [Developer Tools](/categories/developer-tools.md) - [Data & Retrieval](/categories/data-retrieval.md) - [Model Training](/categories/model-training.md) ## Tags llmops, bytewax, comet-ml, docker, large-language-models, aws, generative-ai, machine-learning-engineering ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,991) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,420) - [prompts.chat](/tools/f-prompts-chat.md) - The world's largest open-source prompt library for AI (★ 165,025) - [transformers](/tools/huggingface-transformers.md) - 🤗 Transformers: the model-definition framework for state-of-the-art machine learning models (★ 162,350) - [JavaGuide](/tools/snailclimb-javaguide.md) - Snailclimb/JavaGuide: 面试 & 后端通用面试指南，覆盖计算机基础、数据库、分布式、高并发、系统设计与 AI 应用开发 (★ 156,863) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,311) - [firecrawl](/tools/firecrawl-firecrawl.md) - The API to search, scrape, and interact with the web at scale. (★ 147,150) - [open-webui](/tools/open-webui-open-webui.md) - User-friendly AI Interface (Supports Ollama, OpenAI API, ...) (★ 144,582) ## README (excerpt) ```text

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.

decodingai-magazine%2Fllm-twin-course | Trendshift by Decoding AI

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## 🎯 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

LLM Twin Architecture

### 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](https://www.mongodb.com/) through a series of ETL pipelines. - Send database changes to a [RabbitMQ](https://www.rabbitmq.com/) 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](https://github.com/bytewax/bytewax?utm_source=github&utm_medium=decodingml&utm_campaign=2024_q1) streaming pipeline. - Every message will be cleaned, chunked, embedded and loaded into a [Qdrant](https://qdrant.tech/?utm_source=decodingml&utm_medium=referral&utm_campaign=llm-course) vector DB. - In the bonus series, we refactor the cleaning, chunking, and embedding logic using [Superlinked](https://github.com/superlinked/superlinked?utm_source=community&utm_medium=github&utm_campaign=oscourse), a specialized vector compute engine. We will also load and index the vectors to a [Redis vector DB](https://redis.io/solutions/vector-search/). ### 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](https://www.comet.com/signup/?utm_source=decoding_ml&utm_medium=partner&utm_content=github) experiment tracker to monitor the experiments. - Evaluate the LLM using [Opik](https://github.com/comet-ml/opik) - Save and version the best model to the [Hugging Face model registry](https://huggingface.co/models). - Run and automate the training pipeline using [AWS SageMaker](https://aws.amazon.com/sagemaker/). ### The inference pipeline - Load the fine-tuned LLM from the [Hugging Face model registry](https://huggingface.co/models). - Deploy the LLM as a scalable REST API using [AWS SageMaker inference endpoints](https://aws.amazon.com/sagemaker/deploy/). - Enhance the prompts using advanced RAG techniques. - Monitor the prompts and LLM generated results using [Opik](https://github.com/comet-ml/opik) - In the bonus series, we refactor the advanced RAG layer to write more optimal queries using [Superlin ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/decodingai-magazine-llm-twin-course`](/api/graphcanon/tools/decodingai-magazine-llm-twin-course) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_