6:00 - 6:30 PM - Doors open and pizza
6:30 - 7:30 PM - Erin's talk
7:30 - 8:00 PM - Q&A and networking
Special thanks to Sam and the Betterment team for hosting us!
Although H2O has made it easy for non-experts to experiment with machine learning, there is still a fair bit of knowledge and background in data science that is required to produce high-performing machine learning models. Deep Neural Networks in particular, are notoriously difficult for a non-expert to tune properly. In order for machine learning software to truly be accessible to non-experts, H2O.ai has designed an easy-to-use interface which automates the process of training a large selection of candidate models. Stacked Ensembles will be automatically trained on the collection individual models to produce a highly predictive ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard.
H2O’s AutoML can also be a helpful tool for both the novice and advanced user, by providing a simple function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-preprocessing, feature engineering and model deployment. In this presentation, we will introduce the brand new AutoML functionality in H2O, and demonstrate how to use the functionality from R, Python and Flow (H2O web GUI). AutoML is currently available in the bleeding edge build of H2O and will be officially released in H2O 3.12.
Erin LeDell is a Machine Learning Scientist at H2O.ai, the company that produces the open source machine learning platform, H2O. Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from UC Berkeley. Before joining H2O.ai, she was the Principal Data Scientist at Wise.io (acquired by GE in 2016) and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc.