Part 4 Extending MLOps for large language models

The MLOps foundation you’ve built—containerization, orchestration, experiment tracking, feature stores, and model serving—remains essential for production ML systems. However, large language models (LLMs) introduce new architectural patterns and operational challenges that extend beyond traditional predictive modeling. Retrieval-Augmented Generation (RAG) systems require vector databases for semantic search, prompt management for versioning instructions as code, and specialized safety controls for handling generative outputs. Successfully productionizing LLM applications demands understanding these new components while using your existing infrastructure.