Meeting with Cliff Click: Lessons from a Big Data Machine Learning Startup

Meeting with Cliff Click: Lessons from a Big Data Machine Learning Startup
Speaker: Cliff Click

Date, time, and registration

Date & Time: October 30th, 17:00. Location: UCU campus, Academic building, Lectorium #016. The attendance is open prior to the registration: Please pay attention that the number of seats is limited. The registration form will be closed after the limit will be reached.


I co-founded H2O – and left it ~5 years later (and it’s still an on-going concern), that was about 5 years ago. Along the way we pivoted, rebuilt the entire code base 3 times from scratch. We delivered the fastest K/V store on the planet which is also exact & consistent, parallelized & distributed, with ML algorithms and a clicky-clicky GUI for doing hard-core data science. We ran R on TB of data and Logistic Regression on 7 TB in a few minutes. We survived amazingly broken stuff including crash-on-1st-use in front of Netflix. We raised money at least 3 times, hired and fired (your first fire is always traumatic!). We discovered it’s really hard to make a platform company, much much harder than making a platform. This is a collection of war stories from my many startups.

About the speaker

Cliff Click was the CTO of Neurensic (now successfully exited) and CTO and Co-Founder of (formerly 0xdata), a firm dedicated to creating a new way to think about web-scale math and real-time analytics. He wrote his first compiler when he was 15 (Pascal to TRS Z-80!), although his most famous compiler is the HotSpot Server Compiler (the Sea of Nodes IR). Cliff helped Azul Systems build an 864 core pure-Java mainframe that keeps GC pauses on 500Gb heaps in the micro-second range, and worked on all aspects of that JVM. Before that he worked on HotSpot at Sun, and is at least partially responsible for bringing Java into the mainstream. Cliff is invited to speak regularly at industry and academic conferences and holds a PhD in Computer Science and more than 20 patents. Web resources: personal blog, Linkedin page.

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