Scalable Data Science and Deep Learning with H2O
In a world where Data Science has become a driving force for innovation and profitability, organizations are in an arms race to leverage Machine Learning as a competitive differentiator. H2O is a leading open-source in-memory Machine Learning platform designed for distributed compute clusters. H2O’s vision is to democratize scalable Machine Learning with its open-source Java code base that integrates with everyday tools such as R, Python, Hadoop and Spark. H2O’s friendly User Interface makes it easy for data scientists to train models and share results with business stakeholders. Models can easily be put into production with auto-generated Java scoring code. This talk will spotlight H2O Deep Learning and its ease of use and scalability on large real-world datasets and showcase its versatility across multiple applications.
Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O's Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators.
Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named “2014 Big Data All-Star” by Fortune Magazine. Follow him on Twitter: @ArnoCandel.