What Is MLOps and How Does It Work?

It is essentially the use of software engineering principles, automation, version control, and monitoring, in machine learning, so that models are deployed and continuously run in production rather than built once by hand and left to drift.

DevOps automates building and deploying software, where the code is the artifact. MLOps extends it to ML, where data and models are artifacts too and must be versioned, and where model quality must be monitored for drift, not just uptime.

Since ML models tend to degrade as data evolves, and deploying them by hand is error-prone, MLOps makes deployment repeatable and results reproducible, and keeps models accurate through monitoring and retraining.

Preparing and versioning data, training models in tracked pipelines, deploying them through automated steps, monitoring them in production for drift and errors, and retraining when signals call for it, then repeating the loop.

LLMOps is MLOps specialized for large language models. It shares the same goals of reliable deployment and monitoring but adds concerns specific to LLMs, like prompts, tokens, cost, and guardrails.