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Version: 1.37

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.

Consult the actuals section within the Swagger documentation.

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

{ 'outputs': [<value>] }

or

{ '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