MLOps Pipelines: From Experiment to Production Models

MLOps bridges experimentation and production. Here’s how to run reproducible training and deployment pipelines.

Pipeline Stages

1. Data – Ingest, validate, version (e.g. DVC, Delta Lake).
2. Train – Reproducible training (code + data + config); track experiments (MLflow, Weights & Biases).
3. Evaluate – Holdout metrics, A/B tests, fairness checks.
4. Deploy – Model registry, versioned serving (API or batch); canary or shadow mode.
5. Monitor – Data drift, performance metrics, and alerts.

Reproducibility

• Pin code (git SHA), data (hash or version), and environment (container image).
• Config as code (YAML/JSON) for hyperparameters and feature flags.

Best Practices

• Version models in a registry; promote only after evaluation passes.
• Automate retraining or retraining triggers when drift or performance degrades.
• Secure training data and model artifacts; audit access.

Start with a simple pipeline (train → eval → deploy) and add monitoring and automation as usage grows.