...
Model Registration:
Jupyter: added a new GUI-based Jupyter plugin that provides an intuitive wizard-based approach to register a new model, as well as mechanisms to update existing models, add attachments such as trained model artifacts, and to submit a model for productionization.
CLI: streamlined the approach to register new models via the CLI, with flexible options for adding in schemas, as appropriate.
DataIku DSS: added support to integrate with DataIku’s API’s to onboard code-based Python models created in DataIku DSS.
API: added new API to support registration and persistence of new models, allowing for systematic onboarding of new models, as required.
Model Lifecycle (MLC) OrchestrationAutomation:
MLC Engine: new micro service that automates and manages the common processes in the lifecycle of a model, effectively providing the underpinning for scaled Model Operations (ModelOps). The MLC engine allows for the creation of customized model lifecycle (MLC) processes via a drag and drop user interface, including allowing for defining custom business rules around triggering proactive notifications.
MLC Templates: starter model lifecycle templates to automate and manage common processes in the lifecycle of a model, such as model onboarding, model promotion, on-going backtesting, etc.
Champion / Challenger
Score & Monitor
...
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.
Deprecations:
With the release of v2.0, there a few v1.x items that will be deprecated:
...