Episode 48: Overcoming the Machine Learning Deployment Challenge

It’s been 12 years since Thomas H Davenport and DJ Patil first declared data science to be “the sexiest job of the 21st century” and in that time a lot has changed. Universities have started offering data science degrees; the number of data scientists has grown exponentially; and generative AI technologies, such as Chat-GPT and Dall-E have transformed the world. Yet, throughout that time, one thing has remained the same. Most machine learning projects still fail to deploy. However, it’s not the technical capabilities of data scientists that let them down – those are now better than ever before. Rather, “it’s the lack of a well-established business practice that is almost always to blame.” In this episode, Dr Eric Siegel joins Dr Genevieve Hayes to discuss bizML, the new “gold-standard”, six-step practice he has developed “for ushering machine learning projects from conception to deployment.” Guest Bio Dr Eric Siegel is a leading machine learning consultant and the CEO and co-founder of Gooder AI. He is also the founder of the long-running Machine Learning Week conference series; author of the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die and the recently released The AI Playbook; and host of The Dr Data Show podcast. Highlights (01:21) Challenges in machine learning deployment (05:00) The importance of business involvement in ML projects (15:39) Defining bizML and its steps (25:32) Understanding predictive analytics (26:52) Challenges in model deployment and MLOps (29:12) BizML for generative and causal AI (31:25) Exploring uplift modeling (35:45) Gooder AI: bridging the gap between data science and business value (45:45) Beta testing and future plans for Gooder AI (47:35) Final advice for data scientistsb Links BizML website Connect with Genevieve on LinkedIn Be among the first to hear about the release of each new podcast episode by signing up HERE