MLOps

Machine Learning Operations

DevOps applied to ML -- deployment, monitoring, retraining pipelines.

When you'd see it: Putting machine-learning models into production and keeping them working. MLOps is DevOps for ML.

Why it matters: MLOps covers deploying, monitoring, and retraining models so they keep performing as data shifts. It is the difference between a model that works in a notebook and one that survives in production.

Common mistakes: Treating a deployed model as done. Models drift as the world changes, and without monitoring and retraining, accuracy quietly degrades.