Data Brew by Databricks

Data Brew Season 2 Episode 7: Interpretable Machine Learning

Jul 1, 2021

37:07

Highlights

AI Chapters

Episode notes

Is There a One Clear Answer for Interpretability?

02:29

The Taxonomy of Machine Learning

03:35

Interpretable Machine Learning - The Story Behind This Paper

05:32

Introduction

2min

A Canabal Comes for Full Circle, a Little Bit

3min

Scalability, Scalability and Automation Are Still Really Fundamental Bottlenecks in Machine Learning

3min

Machine Learning and Scale Automation

5min

Machine Learning on Privacy

3min

Machine Learning Is Changing Our Lives a Lot

1min

Interpretable Machine Learning - The Story Behind This Paper

6min

Is There a Better Way to Solve the Interpretability Problem?

2min

Is There a One Clear Answer for Interpretability?

2min

10.

Is There Anything Wrong With Your Methods?

2min

11.

The Taxonomy of Machine Learning

4min

12.

Machine Learning Is Increasingly Important

3min

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

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