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Machine learning was supposed to make things easy by computerizing human cognition. But it made life harder than ever for the data teams tasked with implementing it.

A rising number of enterprises implement machine learning (ML) to improve revenue and operations as they digitally transform their businesses. But ML introduces operational complexities and risks that need careful attention. Data teams must holistically manage the ML lifecycle to make their projects efficient and effective.

This blog kicks off a series that examines the ML lifecycle, which spans (1) data and feature engineering, (2) model…

Kevin Petrie

For 25 years Kevin has deciphered what technology means to practitioners, as an industry analyst, writer, instructor, marketer and services leader.

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