Forrester recently published the Wave™ for Predictive Analytics And Machine Learning Solutions. You can purchase a copy from Forrester for $2,495, or get a free copy here. When Forrester last delivered this analysis in 2015, they called it the Wave™ for Big Data Predictive Analytics Solutions. So, I guess that Big Data is “out” and machine learning is “in.” The chart below shows the
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It’s only March, but already IBM leads the software industry in gasbaggery. Gartner’s most recent Magic Quadrant for Data Science Platforms includes this little gem: Customers are often confused by mismatches between (IBM’s) marketing messages and actual, purchasable products. That’s a polite way to say that IBM marketing messages have enough hot air to float a fleet of balloons over the Bernese
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Gartner recently released its 2017 Magic Quadrant for Data Science Platforms. You can get a copy directly from Gartner if you’re a client, or you can get one for free here, courtesy of SAS. The figure below shows the 2016 and 2017 MQs side by side, with changes shown in red. Here are my comments about the 2016 MQ, written last
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At the 2016 Spark Summit, Gartner Research Director Nick Heudecker asked: Is Spark the Future of Data Analysis? It’s an interesting question, and it requires a little parsing. Nobody believes that Spark alone is the future of data analysis, even its most ardent proponents. A better way to frame the question: Does Spark have a role in the future of analytics?
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As an addendum to my year-end review of machine learning and deep learning, I offer this survey of SQL engines. SQL is the most widely used language for data science according to O’Reilly’s 2016 Data Science Salary Survey. Most projects require at least some SQL operations, and many need nothing but SQL. This review covers six open source leaders: Hive, Impala,
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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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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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Many thanks to readers who voted in the “Bottom ML/DL Story of 2016” poll. The choices were: Amazon Web Services releases DSSTNE to open source Dato rebrands as Turi IBM rebrands an existing Spark service as The Data Science experience IBM rebrands an existing SPSS service as Watson Machine Learning IBM standardizes graph analytics on Apache Tinkerpop Movidius launches
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With minimal fanfare, H2O.ai releases the Steam AI engine, which it describes as “…an end-to-end platform that streamlines the entire process of building and deploying smart applications.” Overall, the software looks like a reasonable product extension for data science teams committed to H2O for machine learning. H2O.ai’s product strategy for Steam has evolved over time; as late as mid-August, the
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The Spark team announces the availability of Spark 2.0.1, a maintenance release with more than 300 stability and bug fixes. Release notes here; list of changes here. Also, Databricks’ Jules Damji publishes the latest bi-weekly roundup of Spark news from around the web; and CERN’s Luca Canali investigates Spark 2.0 performance improvements.
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ML/AI 