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Arnaud Van Looveren - Monitoring machine learning models in production | PyData Global 2020

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Monitoring deployed models is crucial for continued provisioning of high quality ML enabled services. Key areas include model performance monitoring, detecting adversarial instances, outliers and drift using statistical techniques. The talk goes in depth on the algorithmic challenges to monitor models in production and the open source libraries and infrastructure to support these capabilities.

Arnaud leads the data science research effort at Seldon Technologies, focusing on machine learning model interpretability (XAI), outlier, adversarial and drift detection. The team’s work can be found in open source projects Alibi and Alibi Detect. Arnaud recently discussed challenges around monitoring and explaining models in production at the “Challenges in Deploying and Monitoring Machine Learning Systems” workshop at ICML.

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

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