Advanced Computing in the Age of AI|Tuesday, August 4, 2020
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AutoML Platform Kepler Targets Domain Experts 

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As more companies roll out digital infrastructure, they are ingesting greater volumes of data that can be used by business analysts to gauge customer intent and boost transactions. Complexity and lack of data scientists have made that transition harder for mid-size firms looking to monetize “dark” data.

Machine learning vendors are therefore automating key aspects of data science workflows that would allow domain experts to customize pipelines and algorithms based on specific data types. AutoML approaches are promoted as boosting the quantity and quality of machine learning models produced on, say, a monthly basis.

That’s among the goals of a new AutoML platform unveiled this week by Stradigi AI. The Kepler platform simultaneously seeks to address the shortage of data scientists, the resulting inability to move AI models to production and then scale up those models. The strategy focuses on freeing domain experts to select the data science tools needed to get machine models out the door faster, with an initial focus on “high-value” use cases like inventory control or customer churn that are most likely to yield actionable results.

The “sweet spot” for data science automation are mid-size enterprises seeking to reap the rewards of digital transformation, said Per Nyberg, Stradigi’s chief commercial officer. Hence, Kepler automates data science steps to quickly move business analysts up the machine learning curve.

Read the full story here at sister website Datanami.

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

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