Sascha: Welcome back to another episode of The Process & Automation podcast, with The Automation Guys. In today's episode, we will go through a list of questions, questions we often get asked by our audience, on the topic of low code. And hopefully with our answers on these questions, we can break down some of the technical blur which is associated with our industry and automation. So let's kick it off with the first question we have received, um, yeah. Arno, do you like to take the first one?

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.

H2O: A Platform for Big Math From just your laptop to 100's of nodes, H2O gives you a Single System Image - easy aggregation of all the memory and all the cores, and a simple coding style that scales wide at in-memory speeds. H2O is easily 1000x faster than disk based clustering solutions, and often 10x faster than best-of-breed alternative in-memory solutions - and will work directly on your existing Hadoop cluster. H2O ingests a wide variety of formats, parallel and distributed across the cluster, and stores the data highly compressed and then lets you do scale-out math at memory-bandwidth speeds (on compressed data!), making terabyte-scale munging an interactive experience. This is a technical talk on the insides of H2O, specifically focusing on the Single-System-Image aspect: how we write single-threaded code, and have H2O auto-parallelize and auto-scale-out to 100's of nodes and 1000's of cores. Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform.

SF Bay ACM Members: We NEED you to attend! Chapter Elections will be briefly held at the beginning of this meeting. In this talk, Arno Candel presents a brief history of AI and how Deep Learning and Machine Learning techniques are transforming our everyday lives. Arno will introduce H2O, a scalable open-source machine learning platform, and show live demos on how to train sophisticated machine learning models on large distributed datasets. He will show how data scientists and application developers can use the Flow GUI, R, Python, Java, Scala, JavaScript and JSON to build smarter applications, and how to take them to production.