MLOps
MLOps applies engineering discipline to the full machine-learning lifecycle: data, training, deployment, monitoring, and iteration.
Engineering the ML lifecycle
MLOps is the set of practices for reliably building, deploying, and maintaining machine-learning systems in production. It extends DevOps to the particular challenges of ML, where the behavior of a system depends not only on code but also on data and trained model artifacts, all of which change over time and all of which must be versioned, tested, and reproduced.
What makes ML systems hard
A trained model is a function of code, data, and hyperparameters, so reproducing a result requires versioning all three, not just the source. Models silently degrade as data drifts; training and serving can diverge subtly; and the same feature computed differently in the two settings causes train-serve skew. These failure modes are invisible to ordinary software tests and demand ML-specific tooling.
Core practices
- Versioning: track data, features, code, and model artifacts together
- Pipelines: automate data validation, training, and evaluation reproducibly
- CI/CD for models: test and deploy models with the same rigor as code
- Feature stores: compute features once and serve them consistently to training and inference
- Monitoring: watch performance and drift after deployment
- Governance: document models with model cards and track lineage for audit
Deployment and iteration
Models are rolled out carefully: shadow mode runs a new model alongside the old without acting on it, canary releases expose it to a small slice of traffic, and A/B tests compare versions on live outcomes. Because ML systems learn from data they help generate, feedback loops must be watched. Maturity ranges from manual, notebook-driven workflows to fully automated pipelines that retrain and redeploy on triggers, with humans reviewing the results.
MLOps ties together the concerns of reproducible experimentation, fairness auditing, and continuous monitoring into a maintainable practice.