When an organisation has one or two ML models in production, informal management works. Scientists retrain models manually when accuracy drops, deployment is a manual file copy to a server, and monitoring is someone checking a spreadsheet periodically. When an organisation has fifty models in production, this approach collapses. Models silently degrade, retraining provenance is lost, and the engineering team spends more time on operational firefighting than on new development.
MLOps is the set of practices that makes ML operations systematic and scalable. It borrows from DevOps — automation, infrastructure-as-code, continuous deployment, monitoring — and extends these principles to the additional complexity of ML: data versioning, model training reproducibility, experiment tracking, and model performance monitoring.
A mature MLOps platform provides four capabilities. Feature stores centralise and version the engineered features that feed models, ensuring consistency between training and serving and enabling feature reuse across models. Training pipelines automate the full model development process from data ingestion through validation, making model retraining a one-click or automatic operation. Model registries provide a catalogue of all models in production and staging, with metadata about performance, training data, and deployment history. Monitoring infrastructure tracks model performance in real time, alerting when accuracy degrades or input distributions shift.
The tooling landscape for MLOps has matured rapidly. MLflow is the most widely adopted open-source platform for experiment tracking and model registry. Kubeflow provides Kubernetes-native ML workflow orchestration. Feature stores like Feast or Tecton solve the training-serving skew problem. Cloud providers offer integrated MLOps platforms — Amazon SageMaker, Google Vertex AI, Azure ML — that bundle these capabilities with managed infrastructure.
The ROI of MLOps investment compounds over time: each additional model deployed on a mature MLOps platform is cheaper, faster, and more reliable than the first.