The Gap Between a Model and a System
Training an accurate model is a fraction of the work. Serving it reliably, versioning data and features, monitoring for drift, and retraining on fresh data are what make machine learning a durable capability rather than a one-off.
MLOps applies the rigor of modern software delivery — automation, testing, and observability — to the full model lifecycle.
Continuous Training and Deployment
Data changes, and so must models. Automated pipelines retrain on new data, validate performance against held-out benchmarks, and promote a model to production only when it clears quality gates.
Techniques like shadow deployments and canary releases let teams roll out new models safely and roll back instantly if metrics regress.
Monitoring Drift and Cost
A model that was accurate last quarter can quietly degrade as the world changes. Monitoring for data and concept drift catches this early, while cost observability keeps inference spend under control at scale.
The goal is a closed loop: detect, retrain, validate, deploy — with humans firmly in command of the thresholds.