MLOps Basics: From Notebook to Production

A model in a notebook isn't a product

Production ML needs reproducibility, monitoring, and a path to update safely.

The essentials

• Versioning — track code, data, and model versions together.
• Pipelines — automate data prep, training, and evaluation.
• Serving — expose the model behind a reliable API.
• Monitoring — watch for errors and data drift (inputs changing over time).
• Retraining — a plan to refresh the model as the world changes.

Start small

You don't need a platform on day one. A tracked experiment (MLflow/W&B) plus a simple serving API and basic monitoring covers most early needs.