Tag Archives: Amazon Web Services

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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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: 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 One)

This is the first installment in a four-part review of 2016 in machine learning and deep learning. In the first post, we look back at ML/DL news organized in five high-level topic areas: Concerns about bias Interpretable models Deep learning accelerates Supercomputing goes mainstream Cloud platforms build ML/DL stacks In Part Two, we cover developments in each of the leading open

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ICYMI: Top ML/DL Stories 10/3-10/7

Top stories of the week, compiled from daily roundups. Top Reads How to steal a predictive model. Nicole Hemsoth describes 26 emerging applications for deep learning across business, scientific and engineering disciplines. Last week, she covered the next wave of deep learning architectures. Sebastian Raschka publishes Part III of his series on model evaluation, model selection and algorithm selection in

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