Tag Archives: Teradata

Predictions for 2019

It’s that time of year again. Time to drive stakes in the ground about the year ahead. Looking Back First, a brief look back to see if last year’s predictions aged well. You can read them here. Recap below. My predictions come with a lifetime guarantee: if you are not completely satisfied with them, I will return to you, in cash, the

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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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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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