During the model training, the training dataset is used to train the model. Model hyperparameter selection is a major task in the model training process. Models are algorithms, and hyperparameters are the knobs that you can tune to improve the performance of the model. For example, the depth of a decision tree is a hyperparameter.
Model training is discussed in Creating and Importing Model Versions. After a model is trained, you can see the training details on the Model Version Lifecycle page.
- From the Model Versions list, select the model version to view its lifecycle events.
The Model Version Lifecycle page displays. The Import step in the header is marked as completed.
- Select to expand the Training Details section.
The following details display:
Property Description Job ID Specifies the training job ID. You can select View Job Details to see the event details of the job. For details, see Jobs.
Date Specifies the date on which the training job was executed. User Specifies the username who executed the training job. Status Shows the status of the training job as Completed. Dataset ID Displays the training dataset ID used to train the job. You can select View Dataset Statistics and View Dataset to see the dataset details. For details, see Datasets.
Dataset Name Displays the training dataset name used to train the job. Resources Specifies the resources utilized in the training job including CPU and Memory. Hyper Parameters Specifies the hyper parameters defined to run the job including eta and max_depth. Job Progress Lists down all phases of the training job. The job progress information includes: - Job Status: Status of each phase as Created, Scheduled, Running, Trained, Completed
- Start Date: Start date and time for each phase
- End Date: End date and time for each phase
- Duration: Duration of each phase
Training Artifacts Lets you view and download training artifacts. For details, see Viewing and Downloading Model Artifacts.