Scalable Automatic Machine Learning in H2O: Erin LeDell, Chief ML Scientist
Details
Talks are recorded and posted to our YouTube channel at https://www.youtube.com/channel/UCN0kf0sI01-FXPZdWAA-uMA . Coffee is served at the in-person seminar at 101 Howard St.
Title: Scalable Automatic Machine Learning in H2O
Abstract:
The focus of this presentation is scalable and automatic machine learning using the H2O machine learning platform. H2O is an open source, distributed machine learning platform designed for big data. The core machine learning algorithms of H2O are implemented in high-performance Java, however, fully-featured APIs are available in R, Python, Scala, REST/JSON, and also through a web interface. Since H2O's algorithm implementations are distributed, this allows the software to scale to very large datasets that may not fit into RAM on a single machine.
We will provide an overview of the methodology behind H2O's AutoML algorithm. H2O AutoML provides an easy-to-use interface which automates data pre-processing, training and tuning a large selection of candidate models (including multiple stacked ensemble models for superior model performance), and due to the distributed nature of the H2O platform, H2O AutoML can scale to very large datasets. The result of the AutoML run is a "leaderboard" of H2O models which can be easily exported for use in production.
Bio:
Erin LeDell is the Chief Machine Learning Scientist at H2O.ai, the company that produces the open source, distributed machine learning platform, H2O. At H2O.ai, she leads the H2O AutoML project and her current research focus is automated machine learning. Before joining H2O.ai, she was the Principal Data Scientist at Wise.io (acquired by GE) and Marvin Mobile Security (acquired by Veracode), the founder of DataScientific, Inc. and a software engineer. She is also founder of the Women in Machine Learning and Data Science (WiMLDS) organization (wimlds.org) and co-founder of R-Ladies Global (rladies.org). Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley and has a B.S. and M.A. in Mathematics.