MLOps in Production: A Practical Checklist

Most ML projects fail not because the model is bad, but because production infrastructure isn't ready. Here's what we implement on every engagement before go-live.

1. Version everything — Training data, features, model weights, and inference code should all be versioned and traceable. When something breaks at 2am, you need to know exactly what changed.

2. Monitor drift — Data distributions shift. User behavior changes. A model that was 95% accurate in January may be 80% by June. Set up automated drift detection and retraining triggers.

3. Build a rollback path — Champion/challenger deployments let you test new models on a slice of traffic before full rollout. Always have one-click rollback.

4. Log predictions — Store inputs, outputs, and confidence scores. You'll need them for debugging, compliance, and continuous improvement.

5. Human-in-the-loop — Especially for high-stakes decisions, design workflows where AI recommends and humans approve.

At SYNAUCTOR, we bake these into SynaAI Platform and every custom MLOps engagement. Need help getting production-ready? Let's talk.