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Batch Jobs: added support for manually or automatically executing batch jobs, which may include batch scoring jobs, batch validation/metrics jobs, or batch training jobs. Each batch run is monitored, tracked, and has its results persisted with the model’s metadata for traceability.
Runtimes: added support for generic runtimes, which may consist of ModelOp Center’s runtime (called “ModelOp Center Engine”) or external runtimes (e.g. DataIku). ModelOp Center can configure, manage, and monitor model execution across these runtimes, allowing for centralized monitoring across all of your existing and future investments in data/analytic platforms.
Endpoints: added support for generic endpoints to support “always on” running models, including support for REST and Kafka-based endpoints. This complements the ability to run regular batch jobs (see above) for production and testing loads.
Notifications & Alerting: added support for real-time notifications and configurable alerts based on business rules. Notifications and alerts surface vital information about models, their model lifecycles, runtimes, jobs, and other operations-focused items to provide a comprehensive view of the state of your models across the enterprise.
Govern
Model Metadata
Externalized Attachments
Model Test Results
Model Reproducibility Automation
Model Traceability: added support--via the MLC engine--to track each step in a model’s lifecycle for each version of a model to provide a full lineage of a model from when it is registered, to when it is tested, validated, approved, deployed, improved, and eventually retired.
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