MLOps applies software engineering and operations practices to machine learning, so models can be built, deployed and maintained reliably.
Why ML Needs Its Own Practices
A machine learning system depends on code, data and a trained model. All three change, and a model can degrade in production even when no code changes, because the world changes.
Core Practices
- Version everything: code, data, features, models and configuration.
- Reproducible training: pipelines that can retrain a model from scratch.
- Automated testing: code tests plus data validation and model evaluation gates.
- Model registry: a record of trained models, their metrics, lineage and approval status.
- Automated deployment with the ability to roll back.
- Monitoring: data drift, prediction quality, latency and cost.
- Retraining: scheduled or triggered when performance drops.
Maturity Levels
- Manual: notebooks, hand-copied models.
- Automated training pipelines.
- Automated deployment with CI/CD and monitoring.
- Automated retraining with evaluation gates.
Most teams don't need the highest level for every model; match investment to the model's importance.
Roles
Data scientists, ML engineers, data engineers and platform teams share responsibility. Clear ownership of each production model is essential.
Start Simple
Even a small team benefits from versioned data, a training script, a saved evaluation report and basic monitoring before adopting complex platforms.