627: AutoML: Automated Machine Learning

<p>Jon Krohn speaks with Erin LeDell, H2O.ai’s Chief Machine Learning Scientist. They investigate how AutoML supercharges the data science process, the importance of admissible machine learning for an equitable data-driven future, and what Erin’s group Women in Machine Learning &amp; Data Science is doing to increase inclusivity and representation in the field.</p><p>This episode is brought to you by Datalore (<a href='https://datalore.online/SDS'>datalore.online/SDS</a>), the collaborative data science platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit <a href='https://www.jonkrohn.com/podcast'>JonKrohn.com/podcast</a> for sponsorship information.</p><p><br/>In this episode you will learn:<br/>• The H2O AutoML platform Erin developed [07:43]<br/>• How genetic algorithms work [19:17]<br/>• Why you should consider using AutoML? [28:15]<br/>• The “No Free Lunch Theorem” [33:45]<br/>• What Admissible Machine Learning is [37:59]<br/>• What motivated Erin to found R-Ladies Global and Women in Machine Learning and Data Science [47:00]<br/>• How to address bias in datasets [57:03]</p><p><br/>Additional materials: <a href='https://www.superdatascience.com/627'>www.superdatascience.com/627</a></p>