BizML: a framework for success in applied machine learning
In "The AI Playbook," Eric Siegel presents a framework for achieving success in applied machine learning projects.
Despite the interest in using machine learning in applications, most ML projects fail. While the reasons for failures are varied, an important element is the humans who are working on ML projects.
There is a gap between data scientists and business professional and their understanding of what ML can do and how it can be applied to real-world problems.
In his new book The AI Playbook, Eric Siegel, a leading consultant and former Columbia University professor, helps bridge the gap between ML as a science and a business practice.
Siegel provides several key guidelines for applied ML projects, including creating joint teams that include both data scientists and business people. He also introduces bizML, a six-step end-to-end framework for ML projects.
The key idea behind bizML is to work backward, starting with the end business goal and moving your way toward the prediction goal, metrics, and finally ML models.
“For this advanced technology to succeed, we now need improvements in humans—in the way of understanding and leadership—more than in the technology itself,” Siegel writes.
Read all about bizML and The AI Playbook on TechTalks.
Order The AI Playbook on Amazon
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