LLMOps: MLOps for Large Language Models

LLMOps focuses on the operational capabilities and infrastructure required to fine-tune existing foundational models and deploying these refined models as part of a product.

Transfer Learning and Large Language Models LLMs (large language models) have grabbed the public attention with ChatGPT and plenty of companies are looking at opportunities to incorporate similar functionality to their products but perhaps with more added domain expertise and focus. This is possible through transfer learning , where you take an existing state-of-the-art model like GPT-3 and refine it into a model for your specific use case by teaching it with domain-specific data. For example, your use case might have a desired output style and format (e.g. medical notes). Through transfer lea

related reading

• The Llama Hitchiking Guide to Local LLMs – hackerllamaosanseviero.github.io
• GenAI Handbookgenai-handbook.github.io
• Patterns for Building LLM-based Systems & Productseugeneyan.com
• Rise of Large Language Model Operations - by Shabaz Patelshabazpatel.substack.com
• The Ultimate Guide to Fine-Tuning LLMs from Basics to Breakthroughs: An Exhaustive Review of Technologies, Research, Best Practices, Applied Research Challenges and Opportunities (Version 1.0)arxiv.org
• Anatomy of a Modern Finetuning APIbenanderson.work
• Fine-Tuning Llama-2: Tailoring Models to Unique Applicationsanyscale.com
• Fine-tuning a LLM on my blog posts | Didier Lopesdidierlopes.com
• Things we learned about LLMs in 2024simonwillison.net
• 2025: The year in LLMssimonwillison.net
• Mosaic LLMs: GPT-3 quality formosaicml.com
• Getting Caught Up to Modern LLM Research | Samarth Goeldev.samarthgoel.com