Tag Archives: Tibco

Predicting the 2019 MQ

The die is cast. Last month, Gartner selected 16 vendors to include in its 2019 Magic Quadrant for Data Science and Machine Learning. Now, as Gartner prepares to publish the report early next year, I think it will be fun to make some predictions about how each vendor will fare. Some ground rules. I’m not going to talk about DataRobot,

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ML/AI Vendor Roundup: September 2018

Product enhancements, customer references, partnerships, acquisitions, or other significant contributions to machine learning. In general industry news, Forrester releases 2018 “Wave” reports for data science and machine learning. Positive implications for SAS, IBM, RapidMiner, Oracle, and Domino Data Labs. Negative implications for Microsoft, Dataiku, Anaconda, and Google Cloud Platform. Full story here. Vendors listed below in alphabetical order. Dataiku Dataiku

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Forrester’s 2018 PAML “Waves”

Forrester just published two “Wave” reports for predictive analytics and machine learning. The first, covering “multi-modal” solutions, is available here for free. A second report, covering notebook-based solutions, is available here (registration required.) Forrester plans to publish a third report, covering automated machine learning vendors, in 2019. Kudos to Forrester for understanding the diversity of the data science tools market.

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Big Analytics Roundup (August 1, 2016)

There are two big stories this week: Apache Spark 2.0 and Apache Mesos 1.0. There’s also a new release from Kylin, and a nice crop of explainers. IEEE Spectrum publishes its third annual ranking of top programming languages, based on twelve metrics drawn from Google Search, Google Trends, Twitter, GitHub, Stack Overflow, Reddit, Hacker News, CareerBuilder, Dice, and the IEEE

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Big Analytics Roundup (May 23, 2016)

Google announces that it has designed an application-specific integrated circuit (ASIC) expressly for deep neural nets. Tech press goes bananas. The chips, branded Tensor Processing Units (TPUs) require fewer transistors per operation, so Google can fit more operations per second into the chip. In about a year of operation, Google has achieved an order of magnitude improvement in performance per watt for

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Gartner’s 2016 MQ for Advanced Analytics Platforms

This is a revised and expanded version of a story that first appeared in the weekly roundup for February 15. Gartner publishes its 2016 Magic Quadrant for Advanced Analytics Platforms.   You can get a free copy here from RapidMiner (registration required.)  The report is a muddle that mixes up products in different categories that don’t compete with one another,

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