Speaker:: Lina Weichbrodt

Track: General: Production
This talk summarizes the most important insights I gained from running more than 30 machine learning use cases in production. We will take a loan prediction model as an example use case and cover questions like:

– What is the difference between metrics for model training and metrics for model monitoring?
– Which metrics are generally useful to be monitored?
– Which metrics should you prioritize?
– How can monitoring be set up using a traditional software monitoring stack (tools like Grafana and Prometheus)?

This talk will be useful for you if you:
– are a hands-on engineer or data scientist
– want to use your team’s or company’s existing monitoring and dashboarding infrastructure to monitor machine learning stacks
– are a beginner or intermediate in MLOPs

Recorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022.
https://2022.pycon.de
More details at the conference page: https://2022.pycon.de/program/SEXPKA
Twitter: https://twitter.com/pydataberlin
Twitter: https://twitter.com/pyconde

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