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Model Watching: Keeping Your Project in Production

Posted Mar 31, 2021 | Views 613
# Monitoring
# Interview
# Databricks
# Databricks.com
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Speakers

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Ben Wilson
Software Engineer, ML @ Databricks

Tech Lead of the AI OSS Ecosystem team at Databricks - the maintainers of MLflow. Author of Machine Learning Engineering in Action https://www.amazon.com/Machine-Learning-Engineering-Action-Wilson/dp/1617298719/ . Co-host of the Adventures in Machine Learning podcast.

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Demetrios Brinkmann
Chief Happiness Engineer @ MLOps Community

At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.

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SUMMARY

A great deal of time is spent building out the most effectively tuned model, production-hardened code, and elegant implementation for a business problem. Shipping our precious and clever gems to production is not the end of the solution lifecycle, though, and many-an-abandoned projects can attest to this. In this talk, we will discuss how to think about model attribution, monitoring of results, and how (and when) to report those results to the business to ensure a long-lived and healthy solution that actually solves the problem you set out to solve.

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