Champion/Challenger Model Comparison

Owned by Brendan Kelly (Deactivated)
Last updated: Dec 16, 2020 by Dave Trier
2 min read

This section describes the Champion/Challenger feature in ModelOp Center, and how to use it to help determine the best model method or approach to promote to production.

Table of Contents

 

Introduction

Data Science requires experimentation using a variety of methods, approaches, frameworks, and potentially even coding languages. Champion/Challenger is an industry standard technique to compare the performance of competing model strategies, or the performance of a recently updated model against a previous version of the same model. The Champion/Challenger feature in ModelOp Center does a side-by side comparison of the performance of different models in order to determine which model is better to promote to production.

The following phases lay the groundwork for doing a Champion/Challenger comparison.

  1. Define the evaluation metrics for the model. See Model Efficacy Metrics and Monitoring.

  2. Build evaluation tests - this is done through an automated MLC Process. You can build an MLC Process to automatically execute metrics tests for a particular version of a model. See Model Lifecycle Manager Automation for more information.

  3. Conduct a side-by-side comparison of the test results in the Champion/Challenger page of the Command Center.

Champion/Challenger Comparison

For the previously generated metrics results (see steps 1 & 2 above), use the following steps to activate the Champion/Challenger feature in the ModelOp Center to compare test results side-by-side.

  1. In the Command Center, navigate to Models.

  2. Choose the (two or more) models you would like to compare.

     

  3. Select the test results for each of the models

     

  4. You can view the metrics side-by-side to decide which model is performing better.

     

Next Article: Model Efficacy Metrics and Monitoring >