You are part of your organization’s ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating.
When the accuracy of a model deteriorates, the best first step is to conduct champion/challenger testing. This involves deploying a new model (challenger) alongside the current model (champion) to compare their performance. This method helps identify if the new model can perform better under current conditions without immediately discarding the existing model. It provides a controlled environment to test improvements and understand the reasons behind the deterioration. This approach is preferable to directly replacing the model, performing audits, or running red-teaming exercises, which may be subsequent steps based on the findings from the champion/challenger testing.
[Reference: AIGP BODY OF KNOWLEDGE, sections on model performance management and testing strategies., , , ]
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