Generate and Apply Recommendation

With your first datapoint in the Gradient UI, you are now ready to generate your first recommendation and apply it.

1. Click on the Generate button to create your first recommendation

The Generate button will create a new recommendation based on the logs submitted from your last successful job run. If this is your first recommendation, your Gradient status will be "learning", meaning Gradient will train an internal model based on a few test runs of your job.

2. Apply the recommendation to your job

On the right side of the Gradient UI, click on the "Apply" button to automatically update your Databricks job with the recommendation.

3. Re-run your job with the new configuration

Go back to the Databricks console and click on the "run" button for the job being optimized. The Gradient UI should then be populated with its 2nd data point.

4. (optional) Enable Auto-Apply for continuous optimization

To avoid manually applying recommendations, you can also enable Auto-Apply in the "Edit settings" button in the Gradient project page.

If this option is enabled, recommendations will be automatically applied after each run of your job.

Click on the slider to enable Auto-Apply Recommendation. A warning page will pop up to verify this feature. Click on Save.

Auto-training notebook

With the "auto-apply" setting enabled, users can run the Auto-training notebook to quickly run a job back-to-back several times to see the optimization perform. This is the recommended path to completing a "proof-of-concept" experience.

pageAuto-training notebook