Learning AutoML

Foreword by Thomas H. Davenport

Automated machine learning is a difficult subject to write about. It’s a relatively easy concept to grasp at the highest level—“Wouldn’t it be great if a computer could automatically create a statistical model to fit my data well and make great predictions?—but difficult to address in detail from both organizational and technical perspectives. In fact, most experts on AutoML are quite technical in their backgrounds and orientations, and aren’t really able to discuss the organizational and economic implications at all.

This book is different in that it ably discusses both perspectives on the topic. Kerem Tomak is a senior business executive with a hardcore data science background, and he’s able to bridge the two different domains of AutoML. Nevertheless, I would take his advice about what sections of this book to read given your particular background and approach to this topic.

Despite the two worlds of the topic that need to be connected, this is an exciting time to write and read a book on AutoML. Professional data scientists, once wary of AutoML because they thought they could create better models “by hand,” have begun to embrace the technology—particularly for early-stage model exploration.

Among nonprofessionals there has also been an exciting set of new technology developments. Some AutoML programs were already pretty easy to use by amateurs, with point-and-click interfaces and integration with business intelligence programs. But now generative ...