The Importance of AutoML for Augmented Analytics
And the Rise of the Citizen Data Scientist

Augmented analytics is the future of data in the enterprise. This white paper explores how AutoML has developed, the larger part it plays in augmented analytics, and what role it will have in the rise of Enterprise AI, as well as the parallel development of what has come to be known as the citizen data scientist.
 
Accelerated-AI-Modeling
 

 

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From AutoML to Enterprise AI

 

The vision for the future of augmented analytics is one of complete (or nearly complete) automation, where one could feed a dataset and a target to an automated pipeline and get back cleaned data with engineered features, together with the best performing model on top.

This automation of machine learning projects - whether those projects touch company operations, processes, or product development - is the essence of the idea of Enterprise AI and would allow for greatly accelerated AI modeling.

 

 In this white paper, you'll find:

  • A brief definition and history of the rise of AutoML.
  • An exploration of the difference between AutoML and augmented analytics.
  • A look at the parallel development of the citizen data scientist.
  • A list of essential features to look for in AutoML and augmented analytics tools.
  • Recommendations for handling the organizational change associated with augmented analytics.
  • Case studies of AutoML vs. humans.
  • And more!