Presented By O’Reilly and Cloudera
Make Data Work
September 11, 2018: Training & Tutorials
September 12–13, 2018: Keynotes & Sessions
New York, NY

The answer to life, the universe, and everything: But can you get that into production? (sponsored by MapR)

Ted Dunning (MapR)
9:25am–9:35am Wednesday, 09/12/2018
Sponsored
Location: 3E
Average rating: **...
(2.79, 19 ratings)

There’s real value in big data and more waiting when you add real-time, but to get the payoff, you need successful deployments of your machine learning, AI, and data-intensive applications. You need to be ready with your current applications in production but must have an architecture and infrastructure that are ready for the next ones as well.

Production has never been easy, and it’s even more challenging at large scale and distributed across data centers, but it’s being done and done well. Fortunately, new technologies for infrastructure and for data and application management have made it much easier to get systems into production.

Ted Dunning explores how these new tools change how production systems can work and details the tools and techniques real teams are using to build real systems.

This keynote is sponsored by MapR.

Photo of Ted Dunning

Ted Dunning

MapR

Ted Dunning is chief application architect at MapR. He’s also a board member for the Apache Software Foundation, a PMC member and committer on many Apache projects, and a mentor for various incubator projects. Ted has years of experience with machine learning and other big data solutions across a range of sectors. He has contributed to clustering, classification, and matrix decomposition algorithms in Mahout and to the new Mahout Math library and designed the t-digest algorithm used in several open source projects and by a variety of companies. Previously, Ted was chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems and built fraud-detection systems for ID Analytics (LifeLock). Ted has coauthored a number of books on big data topics, including several published by O’Reilly related to machine learning, and has 24 issued patents to date plus a dozen pending. He holds a PhD in computing science from the University of Sheffield. When he’s not doing data science, he plays guitar and mandolin. He also bought the beer at the first Hadoop user group meeting.