🚦 CI/CD for Machine Learning Pipelines

About this simulation

Every commit spawns a build that travels down an automated pipeline, just like a real MLOps system. Unlike ordinary software CI/CD, ML pipelines must also validate the data feeding the model and the model's quality before anything reaches users — a bad dataset or an underperforming model must be caught automatically, the same way a broken unit test blocks a normal deploy.

How it works

• Data Validation — checks incoming training data for schema drift and anomalies. A build is blocked here if simulated drift exceeds the risk threshold.
• Train → Evaluate — the model is trained, then scored against a held-out set. Builds under the accuracy gate are blocked, never reaching the registry.
• Registry — passing models are versioned and stored, ready for staged rollout.
• Canary — a slice of traffic (set by the Canary traffic control) is routed to the new model first, watched for live incidents before a full rollout.
• Auto-rollback — if a canary shows a regression, the system automatically reverts it, keeping bad models out of production, exactly as an automated rollback would in a real deployment pipeline.

Controls

• Commit frequency — how often a new build enters the pipeline.
• Data drift risk — probability that a build's training data fails validation.
• Min accuracy gate — the minimum evaluation score a model must clear to be promoted.
• Canary traffic — the share of promoted builds routed through the canary stage before full production.
• Auto-rollback — toggle whether incidents detected in canary are caught and reverted automatically, or silently ship.

Did you know?

In mature MLOps setups, "tests" include not just code unit tests but data schema checks, statistical drift detectors, and model-quality gates — because a model can pass every code test and still fail catastrophically if the data it was trained on silently changed.