Gartner just released the 2018 Magic Quadrant for Data Science and Machine Learning Platforms. You can get a copy directly from Gartner if you’re a client, or you can get one here, courtesy of RapidMiner. Here are the 2017 and 2018 MQs side by side: Here are my comments on the 2017 and 2016 MQs. For five observations about the 2018 MQ,
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This is the second installment in a four-part review of 2016 in machine learning and deep learning. Part One, here, covered general trends. In Part Two, we review the year in open source machine learning and deep learning projects. Parts Three and Four will cover commercial machine learning and deep learning software and services. There are thousands of open source projects
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Spark Summit 2016 met last week in SFO. There were many cool things; I will publish a separate report when presentations and videos are available. KDnuggets releases results of its annual poll on data science software. Key findings: Python use is up 51%, almost catches up to R, the #1 choice. Excel and Tableau usage are up 47% and 49%,
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Strata + Hadoop World sparks a number of commercial announcements: AtScale has a new release, Microsoft previews R Server on HDInsight, and IBM puts Spark on a mainframe, FWIW. We also have a nice harvest of explainers and perspectives. Slides from Strata available here. The folks at Domino Data ask: Is XGBoost 10X faster than H2O? We’ll never know the answer, since they
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There are two big stories this week, the latest Spark release (story here) and Cloudera’s One Platform announcement. The latter story is big enough to warrant its own section below. I note, however, that Cloudera is simply announcing that it will continue to do what it is already doing: contribute heavily to Spark. Here is a list of IBM’s contributions
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Am on vacation…in lieu of latest news, this scheduled post is a mashup of interesting stories that have crossed my browser. History of Machine Learning The original link is dead, but this graph was included in a related KDnuggets story. I don’t completely agree with the author about the popularity of SVM, which was never as popular as Decision Trees.
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