Online Program

Return to main conference page
Thursday, May 30
Data Science Techologies
Scaling Up Machine Learning to Production
Thu, May 30, 10:30 AM - 12:05 PM
Regency Ballroom AB

Scalable Automatic Machine Learning with H2O (306308)

*Erin LeDell, 

Keywords: machine learning, automatic machine learning, ensemble learning, software, distributed computing

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. H2O currently features distributed implementations of Generalized Linear Models, Gradient Boosting Machines, Random Forest, Deep Neural Nets, Stacked Ensembles (aka "Super Learners"), dimensionality reduction methods (PCA, GLRM), clustering algorithms (K-means), anomaly detection methods, among others.

We will provide a brief overview of the field of Automatic Machine Learning, followed by a detailed look inside 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. R and Python code with H2O machine learning code examples are available on GitHub for participants to follow along on their laptops.