Validate pipelines with real production data before going live

Run multiple pipelines across any infrastructure with a single click

Manage model parameters and configuration

Coordinate interaction between inference and training pipeline

Define flexible policies for model updates and pipeline dependencies

Maintain state and behavior awareness across related pipelines

Identify inaccurate predictions with ML Health indicators

Resolve issues quickly with model snapshots and rollback

Enhance ML performance with rich visualizations and analytics on ML behavior

Diverse teams can communicate with shared and role-based dashboard views

Data scientists codify expertise into familiar operations workflows

MLOps drive feedback from live operations back to data scientists and business analysts