MLOps public preview features¶
This section provides preliminary documentation for features currently in the public preview pipeline. If not enabled for your organization, the feature is not visible.
Although these features have been tested within the engineering and quality environments, they should not be used in production at this time. Note that public preview functionality is subject to change and that any Support SLA agreements are not applicable.
Contact your DataRobot representative or administrator for information on enabling or disabling public preview features.
Available MLOps public preview documentation¶
|Public preview for...||Describes...|
|Tableau Analytics Extension for deployments||Use the Tableau analytics extension to integrate DataRobot predictions into your Tableau project.|
|Multipart upload for the batch prediction API||Upload scoring data through multiple files to improve file intake for large datasets.|
|Remote repository file browser for custom models and tasks||Browse the folders and files in a remote repository to select the files you want to add to a custom model or task.|
|Deployment prediction and training data export for custom metrics||Export a deployment's stored prediction and training data to compute and monitor custom business or performance metrics outside DataRobot.|
|Deployment Usage tab||Tracks prediction processing progress for use in accuracy, data drift, and predictions over time analysis.|
|Drill down on the Data Drift tab||Visualize changes in the drift status over time as a heat map for each tracked feature and monitor the difference in data distribution between time periods for selected features.|
|Expanded batch prediction job definition access||Extend role-based access controls for deployments to the associated batch prediction job definitions.|
|Model logs for model packages||View model logs for model packages from the Model Registry to see successful operations (INFO status) and errors (ERROR status).|
|Model package artifact creation workflow||The improved model package artifact creation workflow provides a clearer and more consistent path to model deployment, with visible connections between a model and its associated model packages.|
|GitHub Actions for custom models||The custom models action manages custom inference models and deployments in DataRobot via GitHub CI/CD workflows.|