Tag Archives: Oracle

2018 in AI/ML

Well, 2018 is dead and gone. Time to take a look back at the year in AI/ML. A reminder that I work for DataRobot. This is my personal blog. Opinions are mine. On the Move It’s hard to believe that Amazon Web Services introduced Amazon SageMaker just a year ago, but here we are. AWS moved aggressively to enhance the

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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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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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The Year in Machine Learning (Part Three)

This is the third installment in a four-part review of 2016 in machine learning and deep learning. In Part One, I 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. In Part Two, I surveyed significant developments in Open Source machine learning projects, such as R, Python,

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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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Big Analytics Roundup (September 19, 2016)

Many thanks to Australia’s Dez Blanchfield for his contributions to this roundup. We set out to create a special “Australia/APAC” edition; however, most of the stories have a global interest: chips are chips and deep learning is deep learning wherever you live. We did find this story, profiling a Tasmanian oyster farm that uses Microsoft’s IoT hub. Well, that’s embarrassing. MapR’s

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Disruption: It’s All About the Business Model

This post is an excerpt adapted from my book, Disruptive Analytics, available soon from Apress and Amazon. (Note: under my contract with Apress I am legally obligated to link to their site, but it’s not yet possible to order the book there. Use the Amazon link if you want the book.) The analytics business is booming. Technology consultant IDC estimates total

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Big Analytics Roundup (July 25, 2016)

We have some more summer reading this week; plus, Splice Machine announces availability of its open source Community Edition, and Google launches two new machine learning APIs. There are so many Spark stories I’ve created a special section for them. Plus we have the usual explainers, perspectives, and news. Quant headhunter Linda Burtch repeats her survey of working analysts in her

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