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Version: 1.34

Performance evaluation with Actuals

Evaluate the performance of a Deployment by comparing predicted outcomes to what we call actuals; ground truth or real-world observations. This may help you determine a Deployment’s effectiveness and identify areas for improvement.

Collect actuals

We assume you have already created a Deployment and made predictions. Use the Deeploy API or Python client to add actuals.

To comply with KServe Data Plane Formatting, the actual format must match the following structure:

{ 'outputs': [<value>] }


{ 'predictions': [<value>] }

Monitor actuals

Monitor actuals for a Deployment on the Performance tab on your Deployment’s Monitoring page. Specifically, you can monitor:

  • Accuracy: for classification models only
  • Root mean squared error (RMSE): for regression models only

Read monitoring a Deployment for more details.

View actuals

View the actual for a specific prediction by clicking on a prediction on the Predictions tab within a Deployment. Scroll down in the Prediction modal to view:

  • the token used to supply the actual
  • the observed value