Almost certainly, your deployed model will degrade over time. Inevitably, as the data used to train your model looks increasingly different from the incoming prediction data, prediction quality declines and becomes less reliable. Challenger models provide a framework to compare alternative models to the current production model. You can submit challenger models that shadow a deployed model, and then replay predictions already made to analyze the performance of each. This allows you to compare the predictions made by the challenger models to the currently deployed model (also called the “champion” model) to determine if there is a superior DataRobot model that would be a better fit. By leveraging the DataRobot MLOps Agent this capability is available for any production model—no matter where it is running and regardless of the framework or language in which it was built. (See this learning session about the MLOps agent.)
Figure 1. Deployment Challengers
To support Challenger models for a deployment, you enable the Challengers option with prediction row storage. To do so, adjust the deployment’s data drift settings either when creating a deployment (from the DataDrift tab) or from the Settings > Data tab for an existing deployment. Prediction row storage instructs DataRobot to store prediction request data at the row level for deployments. DataRobot will use these predictions to compare the champion and challenger models.
Figure 2. Enable challenger models
Before adding a challenger model to a deployment, you first build and select the model to be added as a challenger. You can choose a model from the Leaderboard, or you can use your own custom model deployed within MLOps. In either case, the challenger models are referenced as model packages from the Model Registry and must have the same target type as the champion.
Feature lists for the current champion and the challenger models do not need to match exactly. However, to replay predictions the data passed at prediction time should be a superset of all features required for both the champion and challenger models. For example, if the training dataset for the champion was target, feature 1, feature 2, feature 3, and the training dataset for the challenger was target, feature 1, feature 2, feature 4, then the prediction request for replaying predictions should be: target, feature 1, feature 2, feature 4.
When you have selected a model to serve as a challenger, from the Leaderboard navigate to Predict > Deploy and select Add to Model Registry. This creates a model package for the selected model in the Model Registry, which enables you to add the model to a deployment as a challenger.
Figure 3. Deploy model—add to model registry
Now navigate to the deployment for the champion model, select Challengers, and click Addchallenger model to add any challenger model. Choose the model you want from the Model Registry and click Selectmodelpackage.
Figure 4. Add challenger model Figure 5. Select challenger model from model registry
As the final step you assign a Prediction Environment for the challenger model, which specifies the resources to use for replay predictions. This allows DataRobot MLOps to avoid impacting production performance by replay predictions in an environment other than where the model is hosted.
Figure 6. Prediction Environment for the challenger model
The Deployment Challengers tab shows the following information for the champion and challenger models:
Metadata for each model, such as the project name, model name, and the execution environment type
Actions menu to replace or delete the model
Figure 7. Selected champion and challenger models
After adding challenger models, you can replay stored predictions made with the champion model. This allows you to compare performance metrics such as predicted values, accuracy, and data errors across each model. To replay predictions, select Update challenger predictions.
Figure 8. Replay predictions
The prediction requests made within the time range specified by the date slider will be replayed for the challengers. After predictions are made, click Refresh on the time range selector to view an updated display of performance metrics for the models.
Figure 9. Refreshed display of performance metrics
You can also replay predictions on a periodic schedule instead of doing so manually. Navigate to a deployment’s Settings > Challengers tab. Turn on the toggle to Automaticallyreplaychallengers, and set when you want to replay on predictions (such as every hour or every Sunday at 18:00, etc.).
Figure 10. Set schedule for replaying predictions
Once you have replayed the predictions, you can analyze and compare the results. The Predictions chart (under the Challengers tab) records the average predicted value of the target for each model over time. Hover over a point to compare the average value for each model at the specific point in time. For binary classification projects, use the Class dropdown to select the class for which you want to analyze the average predicted values. The chart also includes a toggle that allows you to switch between continuous and binary modes.
Figure 11. Predictions chart for champion and challenger
The Accuracy chart records the change in a selected accuracy metric value (LogLoss, in this example) over time. These metrics are identical to those used for the evaluation of the model before deployment. Use the dropdown to change accuracy metrics.
Figure 12. Accuracy chart for champion and challenger
The DataErrors chart records the data error rate for each model over time. Data error rate measures the percentage of requests that result in an HTTP error (i.e., problems with the prediction request submission).
For more information on the MLOps suite of tools, visit the DataRobot Community for a variety of additional videos, articles, webinars and more, starting here.
If you’re a licensed DataRobot customer, search the in-app Platform Documentation for Challengerstab.