MLOps Cheat Sheet: 12 Production Patterns You'll Use

The MLOps Pattern You Actually Need

Most MLOps tutorials show you how to build a perfect pipeline. Then production happens.

Your model works in the notebook. It crashes in Docker. The preprocessing that took 2 seconds on your laptop takes 40 seconds on the API server. The monitoring dashboard you spent a week building never caught the bug that cost you three days of bad predictions.

Here are 12 patterns I've used across five production ML systems — not the comprehensive best practices, just the ones that solved actual problems. Some are obvious in hindsight. A few contradict what the documentation recommends. All of them have saved me from 2am debugging sessions.

Pattern 1: Feature Store as a Cache, Not a Database

The first feature store I built tried to be the single source of truth. Every prediction fetched features from Redis, every training job read from S3, everything stayed in sync.

It was architecturally beautiful and operationally fragile.

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