Proper monitoring of machine learning models in production is essential to avoid performance issues. Setting up monitoring can be easy for a single model, but it often becomes challenging at scale or when you face alert fatigue based on many metrics and dashboards.
In this talk, I will introduce the concept of test-based ML monitoring. I will explore how to prioritize metrics based on risks and model use cases, integrate checks in the prediction pipeline and standardize them across similar models and model lifecycle. I will also take an in-depth look at batch model monitoring architecture and the use of open-source tools for setup and analysis.
Affiliation: Evidently AI
Emeli Dral is a Co-founder and CTO at Evidently AI, a startup developing open-source tools to evaluate, test, and monitor the performance of machine learning models.
Earlier, she co-founded an industrial AI startup and served as the Chief Data Scientist at Yandex Data Factory. She led over 50 applied ML projects for various industries - from banking to manufacturing. Emeli is a data science lecturer at GSOM SpBU and Harbour.Space University. She is a co-author of the Machine Learning and Data Analysis curriculum at Coursera with over 100,000 students.
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