H2O Rains with Databricks Cloud

Abstract:

"Big Data science requires powerful tools to produce best possible results: Spark provides computation
platform with powerful data munging API, while H2O provides a set of production ready machine learning algorithms.
The Sparkling Water project combines them together and brings H2O's advanced machine learning algorithms to the Spark ecosystem.
It allows users to use computation power of Spark with machine learning capabilities of H2O including
advanced and production ready algorithms like GLM, DeepLearning, GBM, or GLRM.
This talk demonstrates how to develop and deploy advanced machine learning worklflow using
Databricks platform and Sparkling Water."

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Bio:

Michal is a software engineer who is helping to develop H2O machine learning platform at H2O.ai.
He is JVM and programming languages enthusiast developing software for over 10 years.
After obtaining PhD degree from the Charles University in Prague in 2012 and he spent 1year as a post-doc at Purdue University
developing distributed algorithms for large-scale machine learning.

During his studies he was interested in construction of not only distributed but also embedded and real-time component-based systems using model-driven methods and domain-specific languages.
He participated in design, review and development of various systems including SOFA, Fractal component systems, ESA spaceship on-board architecture or jPapabench control system.