Tag Archives: DataRobot

ML/AI Vendor Roundup October 2018

Hello, everyone! It’s time for another roundup of machine learning and AI news. Product enhancement, customer reference, partnership, acquisition, or other significant contributions. No hype! Vendors listed below in alphabetical order. Algorithmia Algorithmia publishes its first State of Enterprise Machine Learning report based on a survey of 500 data scientists. AWS Machine Learning AWS announces enhancements to SageMaker’s built-in image classifier.

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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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ML/AI Vendor Roundup: August 2018

Product enhancements, customer references, partnerships, acquisitions, financials, or other significant news for ML/AI vendors in August 2018. Alteryx Announces general availability for Release 2018.3. Key new bits: interactive visualization; enhanced Spark integration; a Jupyter notebook for Python scripting. Andrew Brust opines. Releases Q2 financials, revealing 54% YOY topline growth. Amazon Web Services Adds support for custom attributes to SageMaker model

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More Notes on SAS

Last week’s post on SAS provoked numerous comments on this blog, and over on Hacker News. Here are some excerpts. I’ve edited for length and grammar. Feel free to read the originals. SAS employee Scott Mongeau writes: Journeying through the vast graveyard of open source vanity projects I come in on as a mop-up-agent on in any given month, one

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The Year in Machine Learning (Part Four)

This is the fourth installment in a four-part review of 2016 in machine learning and deep learning. — Part One covered Top Trends in the field, including concerns about bias, interpretability, deep learning’s explosive growth, the democratization of supercomputing, and the emergence of cloud machine learning platforms. — Part Two surveyed significant developments in Open Source machine learning projects, such as R, Python, Spark, Flink,

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