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.
Links Mentioned in the Show:
#127 How Data Scientists Can Thrive in Consulting
#126 Make Your A/B Testing More Effective and Efficient
#125 Building Trust in Data with Data Governance
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#124 Using AI to Improve Data Quality in Healthcare
#123 Why We Need More Data Empathy
#122 How Organizations Can Bridge the Data Literacy Gap
#121 ChatGPT and How Generative AI is Augmenting Workflows
#120 Data Trends & Predictions for 2023
#119 Data-Driven Thinking for the Everyday Life
#118 How Power BI Empowers Collaboration
#117 Successful Data & Analytics in the Insurance Industry
#116 Value Creation Within the Modern Data Stack
#115 Inside the Generative AI Revolution
#114 How Chelsea FC Uses Analytics to Drive Matchday Success
#113 Successful Frameworks for Scaling Data Maturity
#112 Data Journalism in the Age of COVID-19
#111 The Rise of the Julia Programming Language
#110 Behind the Scenes of Transamerica’s Data Transformation
#109 How Data Leaders Can Build an Effective Talent Strategy
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