Production ML Is a Systems Problem
Most ML projects don't fail because the model is wrong. They fail because the system around the model is fragile — data pipelines break silently, features drift, monitoring is missing, and rollbacks aren't possible. Shipping ML reliably is a systems problem first and a modelling problem second.
The Production Checklist
- Reproducible training pipelines with versioned data, code, and configuration.
- A feature store that serves the same features in training and inference.
- Shadow deployments before live traffic.
- Continuous evaluation against held-out and recent data slices.
- Clear rollback criteria and automated triggers.
Monitoring Beyond Accuracy
Latency, throughput, data-quality drift, prediction distribution drift, and downstream business KPIs all need eyes on them. A single broken upstream service can silently degrade a model long before headline accuracy moves.
Team Shape
The most productive teams pair applied scientists with platform engineers who own the path to production. The handoff model — scientists "throw a model over the wall" to engineering — is the most reliable predictor of a stalled project.
Closing Thought
Treat your ML stack like any other critical service: with SLOs, runbooks, on-call, and post-incident reviews. The maturity gap between "we trained a model" and "we operate a model" is where most value is created or lost.