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Machine learning models degrade with time. You need to update and retrain them regularly. However, the decision on the maintenance approach is often arbitrary, and the models are simply retrained on a schedule or after every new batch. This can lead to suboptimal performance or wasted resources. In this talk, I will discuss how we can do better: from estimating the speed of the model decay in advance to constructing a proper evaluation set.
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.
PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
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