MLOps

The discipline of operationalizing machine learning models in production: model and data versioning, drift monitoring, automated retraining, rollback, observability, and documentation (model cards). It is to AI what DevOps is to software: what turns an experiment into a reliable system.

When should I use MLOps at a company?

Required from the first model that goes into production—not a luxury to put off until later. Without MLOps, “AI debt” accumulates: no one knows which models are running, why they made decision X, or how to update them.

When is it NOT a good idea MLOps?

Thinking it’s just about tools (MLflow, Weights & Biases, etc.). Tools without rituals or ownership don’t work. MLOps is 60% process and 40% tooling.