Over a decade ago, DevOps came as a response to organizational friction in developing and deploying applications.

Now, MLOps is emerging as a response to similar friction around the consumption and use of ML across the organization.

Feature Highlights

Health Metrics

See critical service health metrics over time to understand model usage and health

Understand drift

Identify important model features that have drifted.

Lifetime accuracy

Monitor model accuracy over its lifetime. Compare predictions to actual values.

Intelligent Alerts

Trigger alerts and notifications to a variety of third-party systems and applications.