Note
Access to this page requires authorization. You can try signing in or changing directories.
Access to this page requires authorization. You can try changing directories.
Use the jobs list view in Azure Machine Learning studio to organize and track your jobs. When you select a job, you can view and analyze its details, such as metrics, parameters, logs, and outputs. This way, you can keep track of your ML job history and ensure a transparent and reproducible ML development process.
This article shows how to complete the following tasks:
- Edit job display name.
- Select and pin columns.
- Sort jobs.
- Filter jobs.
- Perform batch actions on jobs.
- Tag jobs.
Tip
- The Azure Machine Learning CLI v1 reached end of support on September 30, 2025, and SDK v1 is deprecated (end of support June 30, 2026). For v1 information, see How to track, monitor, and analyze jobs (v1). To migrate, see Upgrade to v2.
- For information on monitoring training jobs from the CLI or SDK v2, see Track experiments with MLflow and CLI v2.
- For information on monitoring the Azure Machine Learning service and associated Azure services, see How to monitor Azure Machine Learning.
- If you're looking for information on monitoring models deployed to online endpoints, see Monitor online endpoints.
Important
Items marked (preview) in this article are currently in public preview. The preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. For more information, see Supplemental Terms of Use for Microsoft Azure Previews.
Prerequisites
You need the following items:
-
To use Azure Machine Learning, you need a workspace. If you don't have one, complete Create resources you need to get started to create a workspace and learn more about using it.
Important
If your Azure Machine Learning workspace is configured with a managed virtual network, you might need to add outbound rules to allow access to the public Python package repositories. For more information, see Scenario: Access public machine learning packages.
Run one or more jobs in your workspace to have results available in the dashboard. If you don't have any jobs yet, complete Tutorial: Train a model in Azure Machine Learning.
Enable this preview feature through the preview panel.
View jobs list
- Select Jobs in the left side navigation panel.
- Select either All experiments to view all the jobs in an experiment or select All jobs to view all the jobs submitted in the workspace.
- Select List view at the top to switch into List view.
Job display name
The job display name is an optional and customizable name that you provide for your job. You can edit this name directly in your jobs list view by selecting the pencil icon when you move your mouse over a job name.
Customizing the name helps you organize and label your training jobs.
Select and pin columns
Add, remove, reorder, and pin columns to customize your jobs list. Select Columns to open the column options pane.
In column options, select columns to add or remove from the table. Drag columns to reorder how they appear in the table. Pin any column to the left of the table, so you can view your important column information, such as display name and metric value, while scrolling horizontally.
Sort jobs
Sort your jobs list by your metric values (such as accuracy, loss, or F1 score) to identify the best performing job that meets your criteria.
To sort by multiple columns, hold the Shift key and select the column headers you want to sort. Multiple sorts help you rank your training results according to your criteria.
At any point, manage your sorting preferences for your table in column options under Columns to add or remove columns and change sorting order.
Filter jobs
Filter your jobs list by selecting Filters. You can use quick filters for Status and Created by, or add custom filters to any column, including metrics.
Select Add filter to search or select a column of your preference.
Upon choosing your column, select what type of filter you want and the value. Apply changes and see the jobs list page update accordingly.
You can remove the filter you applied from the job list if you no longer want it. To edit your filters, simply navigate back to Filters to do so.
Perform actions on multiple jobs
Select multiple jobs in your jobs list and perform an action, such as cancel or delete, on them together.
Tag jobs
Tag your experiments with custom labels that help you group and filter them later. To add tags to multiple jobs, select the jobs and then select the Add tags button at the top of the table.
Custom view
To view your jobs in the studio:
Go to the Jobs tab.
Select All experiments to view all the jobs in an experiment, or select All jobs to view all the jobs submitted in the workspace.
In the All jobs page, filter the jobs list by tags, experiments, compute target, and more to better organize and scope your work.
Customize the page by selecting jobs to compare, adding charts, or applying filters. Save these changes as a Custom View so you can easily return to your work. Users with workspace permissions can edit or view the custom view. To enhance collaboration, select Share view to share the custom view with team members.
Next steps
- To learn how to visualize and analyze your experimentation results, see visualize training results.
- To learn how to log metrics for your experiments, see Log metrics during training jobs.
- To learn how to monitor resources and logs from Azure Machine Learning, see Monitoring Azure Machine Learning.