Tag Archives: Databricks

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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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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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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The Year in Machine Learning (Part Two)

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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