Why Operationalizing Machine Learning Requires a Shrewd Business Perspective

The Machine Learning Times (previously Predictive Analytics Times) is the only full-scale content portal devoted exclusively to predictive analytics. It has become a standard must-read and machine learning professionals’ premier resource, delivering timely, relevant industry-leading articles, videos, events, white papers, and community.

In this month’s featured article, Eric Siegel, Ph.D., executive editor of The Machine Learning Times and founder of the Predictive Analytics World and Deep Learning World conference series, discusses the pitfalls of predictive analytics in his article, “Why Operationalizing Machine Learning Requires a Shrewd Business Perspective.”

In his article, Eric warns, “Predictive models often fail to launch. They’re never deployed to drive decisions. This is ultimately a management error. We must pursue the business of machine learning only so that it delivers the business value of machine learning.” and goes on to illustrate how “the analytics and number crunching alone do not determine what to actually do with a model’s predictions – only business acumen can dictate how to best deploy a model.”

Eric also mentions my new book, “Digital Decisioning: Using Decision Management to Deliver Business Impact from AI,” stating “This comprehensive book guides you to leverage the potential of machine learning. It delivers the business-level finesse needed to ensure predictive models are operationalization-ready. It lays the groundwork and sets the standard. It’s a great place to start… and to finish.”

I encourage you to check out Eric’s article and order your copy of “Digital Decisioning: Using Decision Management to Deliver Business Impact from AI” from Amazon today.