#179 Why ML Projects Fail, and How to Ensure Success with Eric Siegel, Founder of Machine Learning Week, Former Columbia Professor, and Bestselling Author

6 snips

Feb 5, 2024

Eric Siegel, a leading consultant and former Columbia University professor, delves into the challenges of deploying machine learning projects. He highlights the troubling statistic that 87% don't make it to production and discusses the critical need for collaboration between technical and business teams. By introducing the BizML framework, he outlines a structured approach for success. Additionally, Siegel warns against the pitfalls of the generative AI hype, urging a balanced perspective on its capabilities while stressing ongoing evaluation to ensure real business impact.

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ADVICE

Focus on Operational Improvement

Treat machine learning projects as operational improvement initiatives.
Prioritize a standardized, collaborative approach involving both data and business teams.

INSIGHT

Technology Fetishization Hinders Progress

An overemphasis on technology, fueled by hype, often overshadows practical deployment concerns.
Failures are often overlooked, hindering progress and genuine value capture.

ADVICE

Bridging the Gap with Semi-Technical Knowledge

Bridge the gap between data teams and business stakeholders by fostering semi-technical understanding.
Focus on what's predicted, how well it's predicted, and how it impacts operations.

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We are in a Generative AI hype cycle. Every executive looking at the potential generative AI today is probably thinking about how they can allocate their department's budget to building some AI use cases. However, many of these use cases won't make it into production.

In a similar vein, the hype around machine learning in the early 2010s led to lots of hype around the technology, but a lot of the value did not pan out. Four years ago, VentureBeat showed that 87% of data science projects did not make it into production. And in a lot of ways, things haven’t gotten much better. And if we don't learn why that is the case, generative AI could be destined to a similar fate.

Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI World, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

In the episode, Adel and Eric explore the reasons why machine learning projects don't make it into production, the BizML Framework or how to bring business stakeholders into the room when building machine learning use cases, the skill gap between business stakeholders and data practitioners, use cases of organizations have leveraged machine learning for operational improvements, what the previous machine learning hype cycle can teach us about generative AI and a lot more.

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