Many business leaders lack a solid understanding of machine learning (ML). Meanwhile, company data scientists often feel disconnected from the business side. In this practical guide, bestselling author and former Columbia University graduate computer science professor Eric Siegel urges business and tech leaders to come out of their silos. He argues that collaboration is vital for organizations hoping to harness the full potential of machine learning models and explains how to apply ML in ways that will transform your organization and optimize your operations.
Business and data professionals must develop a shared understanding of Machine Learning (ML) opportunities.
Seizing machine learning (ML) opportunities requires deep collaboration between both the business and technical sides of your business. If you’re a business professional, you’ll need to develop a holistic understanding of the ML process: You should grasp what the models you’re using predict; how these predictions will affect your operations; the metrics you’re using to determine how accurately your ML project is making predictions; and the kinds of data you’ll need to collect. If you’re a data professional, you must broaden your perspective on ML to understand its power to transform the entire business.
Bridge any gaps between the business and data ends of your organization with bizML — a six-step business approach that Eric Siegel designed to help team members successfully launch and deploy transformative ML projects from a place of shared understanding. If you’re wondering how bizML differs from Machine Learning Operations (MLOps), MLOps centers on the technical side of ML projects...
Eric Siegel is a consultant, who worked formerly as a graduate computer science professor at Columbia University. He is the author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die.
Comment on this summary