Category Archives: Thoughtware

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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Benchmark: Spark Beats MapReduce

A group of scientists affiliated with IBM and several universities report on a detailed analysis of MapReduce and Spark performance across four different workloads.  In this benchmark, Spark outperformed MapReduce on Word Count, k-Means and Page Rank, while MapReduce outperformed Spark on Sort. On the ADT Dev Watch blog Dave Ramel summarizes the paper, arguing that it “brings into question..Databricks Daytona GraySort claim”.  This point refers to Databricks’ record-setting

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O’Reilly Data Science Survey 2015

O’Reilly releases its 2015 Data Science Salary Survey.  The report, authored by John King and Roger Magoulas summarizes results from an ongoing web survey.  The 2015 survey includes responses from “over 600” participants, down from the “over 800” tabulated in 2014. The authors note that the survey includes self-selected respondents from the O’Reilly audience and may not generalize to the

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Spark 1.4 Released

On June 11, the Spark team announced availability of Release 1.4.  More than 210 contributors from 70 different organizations contributed more than 1,000 patches.  Spark continues to expand its contributor base, the best measure of health for an open source project. Spark Core The Spark team continues to improve Spark operability, performance and compatibility.  Key enhancements include: The first phase in

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How to Buy SAS Visual Analytics

Stories about SAS Visual Analytics are among the most widely read posts on this blog.  In the last two years I’ve received many queries from readers who complain that it’s hard to get clear answers about the software from SAS. In software procurement, the customer has bargaining power until the deal closes; after that, power shifts to the vendor.  

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