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CI/CD for Machine Learning

CI/CD for machine learning automates how changes to training code, data pipelines, and model-serving code are tested and released. It adds data and model quality gates to familiar software delivery so a new model reaches production only after its inputs, behavior, and serving contract pass defined checks.

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Don't Panic: CI/CD for Machine Learning

CI/CD for machine learning is a release path for systems whose state is larger than code. Training logic, feature definitions, data expectations, model artifacts, serving images, infrastructure, configuration, and evaluation evidence can all decide whether production is safe. A green build has more paperwork than usual, because the machine learned something and now everyone wants to know from what.

Keep two loops separate. The CI/CD pipeline starts from source change. It tests code, packages components, and deploys pipeline or serving machinery. The ML pipeline starts from approved code and data. It validates data, trains a candidate, evaluates it, and registers an accepted model. Continuous training runs that second loop from a schedule, new data, drift signal, or policy trigger. It is not CI wearing a lab coat.

Fast checks go first because full training is expensive and slow. Unit tests cover feature functions, labels, metrics, and serving adapters. Contract tests check the artifacts passed between steps. A small-data smoke run proves the path executes and emits finite outputs. Only then should the expensive evidence arrive.

The model registry is the handoff point. Approve an immutable model version, not a vague thing named latest. Labels are useful for traffic and workflow, but the audit record needs the exact version underneath. Build once, promote the same artifact, and make rollback point to a known model and serving release.

Machine learning adds a special nuisance: success can be statistical. Metrics need thresholds, tolerances, datasets, and slice checks. Drift is a signal to investigate. It does not prove the current model is bad, and it does not crown the newly trained one king of all predictions.

Start with the Practice Reference to classify loops, gates, and identities. Use the Exercise to design a release path for a feature-logic change. The Cheatsheet is the operational checklist for lineage, promotion, rollback, security, and cost. The goal is not maximum automation. It is a path where every deployed model has a name, evidence, and a safe way back.

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