LLMOps: Operating Large Language Models in Production

Building an AI model is one thing: keeping a large language model running reliably in the real world is another. In this episode, we discuss LLMOps, the emerging set of practices and tools for deploying, monitoring, and maintaining large language models (LLMs) in production. We cover challenges unique to LLMs (like handling the huge model sizes, long context lengths, unpredictable outputs, and continuous updates with new data). You’ll learn about techniques for versioning and evaluating LLMs, setting up feedback loops (human or automated) to catch issues like drift or toxicity, and infrastructure like model hubs and the new Model Context Protocol (MCP) that connects LLMs with external tools and data. We tie it together with examples of how companies manage AI like GPT-4 as a service, ensuring it stays efficient, safe, and up-to-date post-deployment.