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Data Brew Season 2 Episode 7: Interpretable Machine Learning

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Manage episode 296458298 series 2814833
Content provided by Databricks. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Databricks or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

For our second season of Data Brew, we will be focusing on machine learning, from research to production. We will interview folks in academia and industry to discuss topics such as data ethics, production-grade infrastructure for ML, hyperparameter tuning, AutoML, and many more.
What does it mean for a model to be “interpretable”? Ameet Talwalkar shares his thoughts on IML (Interpretable Machine Learning), how it relates to data privacy and fairness, and his research in this field.
See more at databricks.com/data-brew

  continue reading

43 episodes

Artwork
iconShare
 
Manage episode 296458298 series 2814833
Content provided by Databricks. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Databricks or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.

For our second season of Data Brew, we will be focusing on machine learning, from research to production. We will interview folks in academia and industry to discuss topics such as data ethics, production-grade infrastructure for ML, hyperparameter tuning, AutoML, and many more.
What does it mean for a model to be “interpretable”? Ameet Talwalkar shares his thoughts on IML (Interpretable Machine Learning), how it relates to data privacy and fairness, and his research in this field.
See more at databricks.com/data-brew

  continue reading

43 episodes

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