Monitoring and explainability of models in production

Janis Klaise; Arnaud Van Looveren; Clive Cox; Giovanni Vacanti; Alexandru Coca

Monitoring and explainability of models in production

Machine Learning 2020-07-14 v1 Machine Learning

Authors: Janis Klaise , Arnaud Van Looveren , Clive Cox , Giovanni Vacanti , Alexandru Coca

Abstract

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.

Keywords

machine learning statistical inference and model selection statistical data analysis

Cite

@article{arxiv.2007.06299,
  title  = {Monitoring and explainability of models in production},
  author = {Janis Klaise and Arnaud Van Looveren and Clive Cox and Giovanni Vacanti and Alexandru Coca},
  journal= {arXiv preprint arXiv:2007.06299},
  year   = {2020}
}

Comments

Workshop on Challenges in Deploying and Monitoring Machine Learning Systems (ICML 2020)

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