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