Organizational Changes to Improve Machine Learning Success at Your Company
“Even if you don't drink one bit of the A.I. Kool-Aid and you're a senior expert practitioner in machine learning, when you're trying to start a new initiative at your corporation, you're still very liable to make this extremely prevalent mistake that leads to a lack of deployment. And the mistake is that we all, on some level, are fetishizing the technology.”- Eric Siegel In this episode of Data Chats, Chris Richardson interviews Eric Siegel, Ph.D., leading consultant and former Columbia University professor who makes machine learning understandable and captivating. He is also the founder of the Predictive Analytics World and Deep Learning World conference series, which has served more than 17,000 attendees since 2009.They discuss: How to use A.I. to improve your organization’s capabilities and performance Common mistakes business leaders make with understanding machine learning Why top data scientists fail to make successful models most of the time How operational changes lead to improved data analysis What separates a great organization from the rest when it comes to predictive analytics Necessity of socialization and buy-in to successfully implement predictive analysis Ethical implications and risks of machine learning Continue Learning | Data Science for Business LeadersData Science for Business Leaders shows you how to partner with data professionals to uncover business value, make informed decisions and solve problems.Learn More