No matter how clever your learning algorithms, two things will still be true: data and deployment logistics will dominate the effort, and you will need more than two versions of your model, even in full production. However, the rendezvous architecture can help mitigate some of the logistical problems in machine learning.
Ted Dunning offers an overview of the rendezvous architecture, which is geared to deal with much of the complexity involved in deploying models to production, thus allowing more time to be spent thinking and doing real data science. The architecture specifically addresses how strict SLAs can be met even with novice models that you haven’t characterized. The result is that you can bound the risk of experimentation with new models. Ted covers the ideas behind the architecture, practical scenarios, and advantages and disadvantages of the architecture.
Ted Dunning is chief application architect at MapR. He’s also a board member for the Apache Software Foundation, a PMC member and committer of the Apache Mahout, Apache Zookeeper, and Apache Drill 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.
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