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Monitoring and explainability of models in production

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arxiv 2007.06299 v1 pith:YWOWWG3Z submitted 2020-07-13 stat.ML cs.LG

Janis Klaise , Arnaud Van Looveren , Clive Cox , Giovanni Vacanti , Alexandru Coca This is my paper · ORCID
classification stat.MLcs.LG
keywords monitoringareasdatalearningmachinemodelsproductionsolutions
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The machine learning lifecycle extends beyond the deployment stage. Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services. Key areas include model performance and data monitoring, detecting outliers and data drift using statistical techniques, and providing explanations of historic predictions. We discuss the challenges to successful implementation of solutions in each of these areas with some recent examples of production ready solutions using open source tools.

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