BREAKFAST MEETUP! Building Machine Learning Applications with Sparkling Water

Title: Building Machine Learning Applications with Sparkling Water

Abstract: Writing applications which are processing and analyzing large amount of data is still hard. It often requires to design and run Machine Learning experiments in small scale and then consolidate them into a form of application and run them in large scale. There are several distributed machine learning platforms which are trying to mitigate this effort. In this talk we will focus on Sparkling Water which is combining benefits of two platforms – H2O and Spark. H2O is an open-source distributed math-engine providing tuned Machine Learning library, Spark is an execution platform which allows for processing large amount of data. The talk will demonstrate Sparkling Water features and shows its benefits for building rich and robust Machine Learning applications.

Github: https://github.com/h2oai/sparkling-water/tree/master/examples

Speaker: Michal Malohlava, Software Engineer, H2O.ai

Bio: Michal is a geek, developer, Java, Linux, programming languages enthusiast developing software for over 10 years.

He obtained PhD from the Charles University in Prague in 2012 and post-doc at Purdue University.

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 and development of various systems including SOFA and Fractal component systems or jPapabench control system.