OM DETTA AVSNITT
In this episode of Mining Your Own Business, Evan Wimpey chats with Eric Siegel, bestselling author and founder of Machine Learning Week. Tune in as he shares why businesses need to focus on machine learning projects that work in the real world.
Eric also dives into the importance of measuring the impact of machine learning projects, the need for business professionals to understand the technology, and the potential challenges associated with overhyped AI expectations.
In this episode you will learn:
🔹 The importance of deploying machine learning models in real-world business operations to capture value
🔹 Why metrics are key to getting the most out of machine learning projects and impacting business decisions
🔹 The importance of a structured end-to-end practice that enables business stakeholders to collaborate closely with data scientists
🔹 Why tools like generative AI need to be assessed by how they are helping organizations capture real value
Quotes
💬 “Measuring the value is just as fundamental as developing the model. And it goes hand in hand.”
💬 “Measuring the value is just as fundamental as developing the model. And it goes hand in hand.”
💬 “We're trying to improve business operations. We're trying to provide actual business value by implementing change.”
Featured in This Episode
Eric Siegel, Bestselling Author & Machine Learning Week Founder
Eric Siegel, Ph.D. is a consultant, former Columbia University professor, and founder of Machine Learning Week. He’s also an instructor for the “Machine Learning Leadership and Practice” course, executive editor of The Machine Learning Times, and a sought-after keynote speaker. Eric authored the bestselling book “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die” widely used in university courses. His latest book, “The AI Playbook,” is now available at bizml.com.
Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award teaching graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric has appeared on numerous media channels, including Bloomberg, National Geographic, and NPR, and has published in Newsweek, HBR, SciAm blog, WaPo, WSJ, and more.
LinkedIn: linkedin.com/in/predictiveanalytics
Evan Wimpey, Director of Analytics Strategy, Elder Research
Company website: elderresearch.com
LinkedIn: linkedin.com/in/evan-wimpey
Chapters
00:00 Introduction
01:02 Diving into analytics use case examples
05:56 Measuring the effectiveness of analytics efforts
06:49 Discussing Eric’s new book and using metrics to drive decisions
11:37 Talking about best practices around end-to-end processes
14:13 Discussing the need for useful ML models and change management
16:26 Exploring the need for stakeholders and data professionals to understand each other
18:28 Talking about the Machine Learning Week conference
22:58 Delving into Eric’s predictions for future advancements and generative AI
27:24 Chatting about Eric’s dream analytics project and current projects
32:00 Wrapping up the show
Find more show notes, transcripts, & more episodes at:
https://www.elderresearch.com/resource/podcasts/
