For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Without being able to troubleshoot models when they underperform or misbehave, organizations simply won’t be able to adopt and deploy ML at scale.
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