What is MLOps?
The engineering practice that keeps a model working in month nine, not just at launch.
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What is MLOps?
MLOps is the engineering practice of running machine learning models in production: deploying them reliably, versioning models alongside the data they were trained on, monitoring for accuracy drift, retraining on a schedule or trigger, and rolling back safely when a new version underperforms.
Why models need it and software does not
Software fails loudly — an error, an alert, a page. Models fail silently. Accuracy degrades as customer behaviour, product mix or seasonality moves away from the training data, and nobody notices until a business metric shifts for reasons that take weeks to diagnose.
MLOps exists to make that failure visible.
What it covers
- Reproducible training pipelines defined as code.
- Versioning of model artefacts and the data snapshot behind them.
- Continuous evaluation against held-out data, with alerting on degradation.
- Automated retraining with a human gate before promotion.
- A rollback procedure that has actually been tested.
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