ModelOps supports partitioned (micro) modeling where an individual model is trained on a specific partition of the data for each model version. For example, if you have a dataset of product sales and you want to create a predictive model for future sales for every product, you can use micro modeling.
A model can have thousands or even millions of model partitions, though it is represented as one model version in the UI. A micro model is recognized by the fact that it generates a partition artifact after the training.
- From the Model Versions list, select a micro model version.
The Model Version Lifecycle page displays.
- Expand the Training Details section.
The training details section expands showing all details of the model version training.For details of the model version lifecycle, see Model Lifecycle.
- Select the View training details for model partitions link under Training artifacts.The Model partitions list displays the following details:
Property Description Partition ID Specifies the partition ID. Partition records Specifies the number of records in the partition. Total partitions Displays the total number of partitions of the model version. Total records Displays the total number of records in all partitions. - Select Select hyper parameters to select hyper parameters to display in the Partitions list.
- Hover on a field in the Available fields list and select
.The field adds to the Selected fields list. - Add all the required fields to the Selected fields list and click Select.The values of the selected parameters display in the Partitions list for all the partitions.