From CI/CD to MLOps: Production Workflows for Machine Learning

01Software engineering caught up with ML — now ML must catch up

Ten years ago the question was whether DevOps principles could carry machine learning. Today the answer is yes, with one twist: ML adds a data dimension that code-only pipelines never had. Models are code plus data plus configuration, and all three drift over time.

Your existing CI/CD muscle — version control, review, staging, rollback — applies perfectly to ML code. The new work is handling the artifacts, the experiments, and the performance monitoring that make ML a living system.

02MLOps is CI/CD with three extra pillars

Treat every model like an application deployable, so it benefits from everything you already do for web services.

• Versioning: code, data, and model artifacts all get immutable, traceable versions from the same pipeline.
• Reproducibility: an experiment is only valid if a teammate can rebuild it from the recorded recipe.
• Registry: a model registry (MLflow or similar) as the single source of truth for what is promoted to production.
• Monitoring: track data drift, prediction quality, and latency in production — not just training metrics.

03A practical MLOps pipeline

The pipeline we ship for clients is deliberately boring, because boring pipelines run. It looks like this: data validation triggers a training job in a clean container; training logs every hyperparameter and metric to the registry; a promotion gate evaluates the eval set and blocks poor candidates; the winning artifact is packaged into a serving image; the deployment rolls out behind a feature flag; and a monitoring stack watches drift and quality continuously.

• Data validation first — a broken feature in the source data beats any modeling fix.
• Containerize training and serving identically to remove 'works on my laptop' drift.
• Rollback is a container version — never retrain to fix a serving regression.
• Alert on drift before users notice degradation; your support queue is not your monitoring system.

04Where the cloud matters

For most workloads a managed stack on AWS, Azure, or GCP — managed Kubernetes, a managed registry, and object storage with versioning — removes weeks of operational work and costs far less than a DIY cluster. Reserve bespoke infrastructure for latency-critical, high-throughput inference where you can prove the savings.

Whatever you choose, the principles stay the same: everything reproducible, everything versioned, everything observable. If a stakeholder asks 'what model is serving right now?', your answer should be one command away.