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What MLOps for production machine learning actually covers once a model leaves the notebook — versioning, drift detection, and the retraining loop that keeps accuracy from decaying quietly.
MLOps for production machine learning covers everything that has to happen to a model after it performs well in a notebook: versioning not just code but data and the trained model artifact together, automating the retraining pipeline, and monitoring the live model's accuracy against production data rather than the test set it was validated on originally.
The reason MLOps best practices aren't just DevOps applied to models: a model's behaviour degrades even when no code changes, because the real-world data it sees in production drifts away from the distribution it was trained on. A CI/CD pipeline that only re-tests code on every commit has no way to catch that — it needs a parallel pipeline watching the data itself.
Model drift detection running continuously against live traffic — statistical tests comparing the distribution of production inputs (and, where ground truth eventually arrives, outputs) against the training baseline, with an alert threshold set before drift becomes a visible accuracy problem, not after.
ML pipeline monitoring covering the full path from data ingestion through feature computation to model serving — a broken upstream data feed that silently starts sending nulls or defaults is a far more common production failure than the model itself degrading, and it's invisible to any monitoring that only watches the model's output.
Model versioning that ties a specific trained artifact to the exact training-data snapshot, feature-engineering code and hyperparameters that produced it — the only way to answer why the model made a given decision six months later, or to roll back cleanly when a new version underperforms.
A retraining trigger tied to a measured drift or performance threshold, not a fixed calendar schedule that retrains too often on stable data or too rarely on fast-moving data.
Shadow deployment of a new model version against live traffic before it takes over serving, so a regression shows up in monitoring before it shows up in a customer-facing outcome.
A single source of truth for which model version is live in which environment — a surprising number of production incidents trace back to nobody being certain which version was actually serving traffic.
DevOps assumes behaviour only changes when code changes. MLOps has to account for a second axis: a model's real-world accuracy can degrade with zero code changes, purely because the data it sees in production has drifted from its training data. That's why MLOps pipelines version and monitor data and models, not just code.
This page summarises an engineering approach for orientation purposes and reflects our understanding as of the review date above. AI tooling, model capabilities and best practices move quickly; validate specifics against current vendor documentation and your own environment before committing to an architecture.
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Statistically — comparing the distribution of live production inputs (and outputs, once ground truth is available) against the distribution the model was trained and validated on, with an alert threshold set in advance rather than discovered after accuracy has already visibly dropped.
A lighter version of it, yes. Even one model needs versioning, basic drift monitoring and a defined retraining trigger — skipping these because there's 'only one model' is exactly how a single quietly-degrading model goes unnoticed for months.