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