Model Metrics Metrics are essential for tracking model performance. By using custom code, you can track specific model predictions and analyze the metrics. Enabling model metricsMetrics are used to track the performance of the models. When you enable model metrics while creating a workbench, the metrics are stored in a scalable metrics store. You can track individual model predictions and analyze metrics using custom code.Tracking model metrics without deploying a modelCloudera recommends that you develop and test model metrics in a workbench session before actually deploying the model. This workflow avoids the need to rebuild and redeploy a model to test every change.Tracking metrics for deployed modelsWhen you have finished developing your metrics tracking code and the code that consumes the metrics, simply deploy the predict function from predict_with_metrics.py as a model. No code changes are necessary.
