The era of Big Data has passed, and the era of sensory overload – that is, the proliferation of sensor data – is upon us. The challenge today is how to create the next generation of business and consumer applications that transform how we interact with sensors themselves. Applications need to learn from every user interaction and data point and predict what can happen next. The future depends on Machine Learning, as much as it depends on the data itself, to change the way we interact with these systems.
In this talk, we explain H2O’s scalable distributed in-memory math architecture and its design principles. The platform was built alongside (and on top of) both Hadoop and Spark clusters and includes interfaces for R, Python, Scala, Java, JavaScript and JSON, along with its interactive graphical Flow interface that make it easier for non-engineers to stitch together complete analytic workflows. We outline the implementation of distributed machine learning algorithms such as Elastic Net, Random Forest, Gradient Boosting and Deep Learning. We will present a broad range of use cases and live demos that include world-record deep learning models, anomaly detection tools and approaches for Kaggle data science competitions. We also demonstrate the applicability of H2O in enterprise environments for real-world customer production use cases. By the end of this presentation, you will know how to create your own machine learning workflows on your data using R, Python (iPython Notebooks) or the Flow GUI.
Presenter Bio
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.
