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June 29th, 2023

Regression projects, toasts, and artifact retrieval

Introducing support for regression tasks 📈

This week we shipped a huge set of features and improvements, including our solution for regression projects!

Finally, you can use Openlayer to evaluate your tabular regression models. We’ve updated our suite of goals for these projects, added new metrics like mean squared error (MSE) and mean absolute error (MAE), and delivered a new set of tailored insights and visualizations such as residuals plots.

This update also includes an improved notification system: toasts that present in the bottom right corner when creating or updating goals, projects, and commits. Now, you create all your goals at once with fewer button clicks.

Last but not least, you can now download the models and datasets under a commit within the platform. Simply navigate to your commit history and click on the options icon to download artifacts. Never worry about losing track of your models or datasets again.

New features

  • Added support for tabular regression projects
  • Toast notifications now present for various in-app user actions, e.g. when creating projects, commits, or goals
  • Enabled downloading commit artifacts (models and datasets)
  • Allowed deleting commits


  • Improved graph colors for dark mode
  • Commits within the timeline now show the time uploaded when within the past day
  • Commit columns in the timeline are now highlighted when hovering

Bug fixes

  • Sentence length goals would not render failing rows in the goal diagnosis modal
  • Filtering by non-alphanumeric symbols when creating performance goals was not possible in text classification projects
  • Changing operators would break filters within the performance goal creation page
  • Heatmap labels would not always align or overflow properly
  • Buggy UI artifacts would unexpectedly appear when hovering over timeline cells
  • Sorting the timeline would not persist the user selection correctly
  • Quasi-constant feature goals would break when all features have low variance
  • Selection highlight was not visible within certain input boxes
  • NaN values inside categorical features would break performance goal subpopulations
  • Heatmaps that are too large across one or both dimensions no longer attempt to render
  • Confidence distributions now display an informative error message when failing to compute