Presented By O'Reilly and Cloudera
Make Data Work
September 26–27, 2016: Training
September 27–29, 2016: Tutorials & Conference
New York, NY

The flux capacitor of machine learning: Turn data garbage into 1.21 gigawatt-powered acceleration

Ingo Mierswa (RapidMiner)
1:15pm–1:55pm Wednesday, 09/28/2016
Location: 1B 01/02

The flux capacitor was the core component that made time travel possible in Back to the Future, processing garbage as a power source. Did you know that you can achieve the same affect in machine learning? Ingo Mierswa, RapidMiner’s cofounder and CEO, offers a case study on how he took “garbage” data drawn from 250K data scientist RapidMiner users and through machine learning turned it into Wisdom of Crowds, which helps novice and expert data scientists alike accelerate the creation of their predictive models by delivering expert recommendations about what other scientists would do at every step in their predictive analytics process. Ingo covers the most frequently used machine-learning techniques, what data preparation most experts perform before modeling, and how those behaviors have changed over time, along with other interesting patterns.

This session is sponsored by RapidMiner.

Photo of Ingo Mierswa

Ingo Mierswa


Ingo Mierswa is a veteran data scientist and the cofounder and CEO of RapidMiner, the company he developed in the Artificial Intelligence Division of TU Dortmund, Germany. Ingo is responsible for strategic innovation at RapidMiner, deals with all big picture questions around its technologies, and serves on the board. Under his leadership, RapidMiner has grown up to 300% per year over the first seven years. In 2012, he spearheaded the go-international strategy with the opening of offices in the US, the UK, and Hungary. After two rounds of fundraising, the acquisition of Radoop, and supporting the positioning of RapidMiner with leading analyst firms like Gartner and Forrester, Ingo takes a lot of pride in bringing the world’s best team to RapidMiner. Ingo has also authored numerous award-winning publications about predictive analytics and big data.