{"id":9847,"date":"2024-04-24T15:24:26","date_gmt":"2024-04-24T23:24:26","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=9847"},"modified":"2025-04-29T12:25:27","modified_gmt":"2025-04-29T12:25:27","slug":"streaming-pipelines-for-fine-tuning-llms","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/streaming-pipelines-for-fine-tuning-llms\/","title":{"rendered":"SOTA Python Streaming Pipelines for Fine-tuning LLMs and RAG – in Real-Time!"},"content":{"rendered":"\n

Welcome to Lesson 4 of 12 in our free course series, LLM Twin: Building Your Production-Ready AI Replica. You\u2019ll learn how to use LLMs, vector DVs, and LLMOps best practices to design, train, and deploy a production ready \u201cLLM twin\u201d of yourself. This AI character will write like you, incorporating your style, personality, and voice into an LLM. For a full overview of course objectives and prerequisites, start with Lesson 1.<\/a><\/em><\/p>\n\n\n\n

Lessons<\/strong><\/p>\n\n\n\n

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  1. An End-to-End Framework for Production-Ready LLM Systems by Building Your LLM Twin<\/a><\/li>\n\n\n\n
  2. Your Content is Gold: I Turned 3 Years of Blog Posts into an LLM Training<\/a><\/li>\n\n\n\n
  3. I Replaced 1000 Lines of Polling Code with 50 Lines of CDC Magic<\/a><\/li>\n\n\n\n
  4. SOTA Python Streaming Pipelines for Fine-tuning LLMs and RAG \u2014 in Real-Time!<\/a><\/li>\n\n\n\n
  5. The 4 Advanced RAG Algorithms You Must Know to Implement<\/a><\/li>\n\n\n\n
  6. Turning Raw Data Into Fine-Tuning Datasets<\/a><\/li>\n\n\n\n
  7. 8B Parameters, 1 GPU, No Problems: The Ultimate LLM Fine-tuning Pipeline<\/a><\/li>\n\n\n\n
  8. The Engineer\u2019s Framework for LLM & RAG Evaluation<\/a><\/li>\n\n\n\n
  9. Beyond Proof of Concept: Building RAG Systems That Scale<\/a><\/li>\n\n\n\n
  10. The Ultimate Prompt Monitoring Pipeline<\/a><\/li>\n\n\n\n
  11. [Bonus] Build a scalable RAG ingestion pipeline using 74.3% less code<\/a><\/li>\n\n\n\n
  12. [Bonus] Build Multi-Index Advanced RAG Apps<\/a><\/li>\n<\/ol>\n\n\n\n

    In the 4th lesson<\/strong>, we will focus on the feature pipeline.<\/strong><\/p>\n\n\n\n

    The feature pipeline<\/strong> is the first<\/strong> pipeline<\/strong> presented in the 3 pipeline architecture<\/strong>: feature, training and inference pipelines.<\/p>\n\n\n\n

    A feature pipeline<\/strong> takes raw data as input, processes it into features, and stores it in a feature store, from which the training & inference pipelines will use it.<\/p>\n\n\n\n

    The component is completely isolated from the training and inference code. All the communication is done through the feature store.<\/p>\n\n\n\n

    To avoid repeating myself, if you are unfamiliar<\/strong> with the 3 pipeline architecture<\/strong>, check out Lesson 1 for a refresher.<\/em><\/p>\n\n\n\n

    By the end of this<\/strong> article<\/strong>, you will learn<\/strong> to design<\/strong> and build<\/strong> a production-ready feature pipeline<\/strong> that:<\/p>\n\n\n\n