MLOps
MLOps is the set of practices, tools and pipelines that take a machine learning model from a data scientist's notebook into production and keep it running reliably: versioning data and code, training, evaluating, deploying and monitoring the model like any other business-critical software.
No jargon, no math
In plain words, what is it?
MLOps treats a model like a product on a production line, not a file on a laptop. What goes in: versioned data and versioned training code. The pipeline then runs the same loop every time: train on the data, evaluate on a test set the model has never seen, and only if the new version beats the one in production does it get packaged as a numbered, reproducible artefact. That artefact is deployed behind an API, and from that moment monitoring watches its live predictions for accuracy, latency and errors. When performance slips, or the world changes underneath it, the loop restarts: fresh data, retrain, re-evaluate, redeploy. The whole point is repeatability: anyone can answer “which data, which code, which version made this prediction?”. Where it breaks: if data and code are not versioned, you get a model nobody can reproduce or debug.
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Version. Data and training code are versioned together, so every model is reproducible.
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Train. The pipeline trains a candidate on the versioned data, on schedule or on demand.
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Evaluate. The candidate is scored on a held-out test set against the production model.
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Deploy. Only a better candidate ships, usually to a small traffic share first.
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Monitor. Live accuracy, latency and errors are tracked on dashboards.
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Retrain. When performance slips, the loop restarts with fresh data.
Terms that live nearby.
Data Pipeline
A data pipeline is the connected chain of steps that carries data from where it is produced to where it is consumed: ingest, validate, transform, store and serve, each stage handing clean, well-formed data to the next.
ReadModel Evaluation
Model evaluation is the process of measuring how well a model performs on data it has never seen, using metrics and test sets agreed in advance, before you trust it with real decisions.
ReadModel Drift
Model drift is the silent loss of accuracy that happens when live data stops resembling the data a model was trained on: the world moves, the model stays still.
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