DevOps automates building, testing and running code, while MLOps extends that practice to the data, training and evaluation that machine learning models need. MLOps also tracks model drift after release, a problem ordinary application code does not face.
DevOps is a way of building and running software in which development and operations work as one team. Automation, shared ownership and fast feedback let that team ship changes often and safely.
A feature store is a system that stores, documents and serves the input features used by machine learning models. It keeps them consistent between training and live predictions.
A machine learning engineer builds, deploys and maintains machine learning models as reliable software in production. Teams usually hire one once a model has proved its value and must run, scale and stay accurate every day.
Model drift is the decline in a machine learning model's accuracy over time. It happens when the data or real-world patterns seen in production move away from those it was trained on.
MLOps is the practice of deploying, monitoring and updating machine learning models in production. It applies DevOps discipline to models and the data they depend on, so releases are repeatable, auditable and reversible.
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