Scalable Automatic Machine Learning in H2O

Hello Makers,

We're back with another meetup on one of the most talked about topics in the industry -- Automatic Machine Learning. Join us as Erin LeDell from H2O.ai delves into the topic.

We'll also be giving away two tickets (valued at $395 each) to our upcoming H2O World 2017 (http://h2oworld.h2o.ai/)!

Agenda
6:30 - 7:00 PM - Doors open and pizza
7:00 PM - 7:45 PM - Erin’s talk
7:45 PM - 8:00 PM - Q&A and networking

Description:
In recent years, the demand for machine learning experts has outpaced the supply, despite the surge of people entering the field. To address this gap, there have been big strides in the development of user-friendly machine learning software that can be used by non-experts. Although H2O and other tools have made it easier for practitioners to train and deploy machine learning models at scale, 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 this presentation, we provide an overview of the the field of "Automatic Machine Learning" and introduce the new AutoML functionality in H2O. H2O's AutoML provides an easy-to-use interface which automates the process of training a large, comprehensive selection of candidate models and a stacked ensemble model which, in most cases, will be the top performing model in the AutoML Leaderboard.

H2O AutoML (http://docs.h2o.ai/h2o/latest-stable/h2o-docs/automl.html) is available in all the H2O interfaces including the h2o R package, Python module and the Flow web GUI. We will also provide simple code examples to get you started using AutoML.

Erin’s Bio:

Erin is a Statistician and Machine Learning Scientist at H2O.ai. She is the main author of H2O Ensemble. Before joining H2O, she was the Principal Data Scientist at Wise.io and Marvin Mobile Security (acquired by Veracode in 2012) and the founder of DataScientific, Inc. Erin received her Ph.D. in Biostatistics with a Designated Emphasis in Computational Science and Engineering from University of California, Berkeley. Her research focuses on ensemble machine learning, learning from imbalanced binary-outcome data, influence curve based variance estimation and statistical computing. She also holds a B.S. and M.A. in Mathematics.

This meetup will be recorded and will be available within 3-5 days. We will post the recording on this page and send a note to all those who RSVP'd.