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Refreshed Web UI (“Command Center”): incorporated a new overall layout and design to streamline the user experience and provide additional details about all models, their model lifecycles, and the model runtimes. This includes an Operations-focused landing page to surface critical issues and notifications for proactive attention to ensure your models are providing the highest value possible.
CLI: revamped command line interface (CLI) to allow those that prefer interacting via a terminal to have easy access to interact with ModelOp Center, including getting model and job status, registering new models, adding new assets, and many more features.
Model Factory Plugins: added new Jupyter and DataIku plugins to provide Data Scientists streamlined access to register and update models within ModelOp Center directly from their favorite model factory.
Updated architecture: updated the core architecture that includes a number of new micro services, an enhanced service registry, a best-in-class gateway, and other enhancement to further enable scale and extensibility for the enterprise.
Deploy
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) Orchestration
Champion / Challenger
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