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 data pipeline is an automated series of steps that moves data from where it is created to where it is stored, analysed or used. Steps along the way may extract, clean, reshape or enrich it.
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.
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.
Training data is the set of examples a machine learning model learns from. Its quality, coverage and labelling largely determine how well the model performs.
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.
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