CI/CD for Machine Learning: How MLOps Differs

MLOps is CI/CD with extra stages: data validation, model eval, drift monitoring. Same discipline; broader scope.

Why ML CI/CD differs

ML pipelines pass data through training; output models; eval against benchmarks; deploy.

Each stage has failure modes that traditional CI/CD does not handle.

Four extra stages

• 1. Data validation. Schema, distribution, freshness.
• 2. Model training. Reproducible; tracked experiments.
• 3. Model evaluation. Against benchmark + production-like sets.
• 4. Deployment + drift monitoring. Watch for performance degradation.

Tooling per stage

Validation: Great Expectations, Pandera.

Training: MLflow, Weights & Biases, Kubeflow.

Eval: custom + standard benchmarks.

Drift: Evidently, Arize, WhyLabs.

Team structure

Cross-functional team: data engineers, ML engineers, platform engineers. Each owns a stage.

Without cross-functional ownership, models stall between data team and platform team.

Antipatterns

• ML deployment via standard CI. Misses validation stages.
• No drift monitoring. Models silently degrade.
• One person owns the full pipeline. Bus factor 1.

What to do this week

Three moves. (1) Apply this to one pipeline first. (2) Measure deploy frequency / MTTR before/after. (3) Document the outcome so the next team starts from data.