MLOps Basics: Deploying and Monitoring ML Models

A model that scores 95% in a notebook is worthless until it's served, monitored, and maintained. MLOps is the discipline of running models in production reliably. Here are the fundamentals.

1. Version Everything

Reproducibility is the foundation. Version your data, your model artifacts, and your training code together so any prediction can be traced back to exactly what produced it.

2. Serve the Model

• Real-time: wrap the model in an API (FastAPI, BentoML) behind a load balancer.
• Batch: score large datasets on a schedule when latency doesn't matter.
• Containerize so the serving environment matches training exactly.

3. Monitor for Drift

Models decay as the real world changes. Track input distributions (data drift) and prediction quality (concept drift). When live data diverges from training data, accuracy quietly drops — monitoring is how you catch it before users do.

4. Automate Retraining

Set up a pipeline that retrains on fresh data, evaluates against a held-out set, and promotes the new model only if it beats the current one. This closes the loop and keeps performance from degrading over time.