MLOps

MLOps (Machine Learning Operations) is a set of practices, tools and processes that automate and standardise the entire lifecycle of machine learning models — from data ingestion, through training and validation, to production deployment and monitoring. MLOps brings the discipline familiar from DevOps into the world of artificial intelligence, where alongside code you also version data and the trained models themselves.

How MLOps works

A classic application pipeline operates on code alone. In machine learning, three variables drive the outcome: code, training data and the model artifact itself. MLOps binds them into a repeatable, auditable process:

• Data and model versioning — each model version is tied to a specific dataset and hyperparameters, making experiments reproducible and rollbacks possible.
• Continuous training (CT) — an extension of CI/CD that automatically retrains a model when new data arrives or prediction quality drops.
• Model registry — a central repository of approved model versions ready for deployment.
• Monitoring and drift detection — watching prediction quality and shifts in the input distribution (data drift) that quietly erode a model's accuracy.

MLOps in practice

MLOps proves its worth anywhere a model reaches production and has to run reliably:

1. Recommendation and scoring systems — automatic retraining on fresh data keeps predictions accurate.
2. Fraud detection — fast rollback to a previous model version when a new one raises too many false positives.
3. Containerised deployment — models packaged into Docker images and served as an API, unifying the training and production environments.

A solid infrastructure foundation — compute-capable servers, predictable networking and isolated environments — is a prerequisite for an MLOps pipeline to run repeatably and without surprises.