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:
#108 The Hallmarks of Successful Data Training Programs
#107 The Deep Learning Revolution in Space Science
#106 How CBRE is Increasing Data Literacy for Over 3,000 Employees
#105 What Data Visualization Means for Data Literacy
#104 How the Data Community Can Accelerate Your Data Career
#103 How Data Literacy Skills Help You Succeed
Announcing Data Literacy Month
#102 How an Always-Learning Culture Drives Innovation at Shopify
#101 How Real-Time Data Accelerates Business Outcomes
#100 Embedded Machine Learning on Edge Devices
#99 Post-Deployment Data Science
#98 Interpretable Machine Learning
#97 How Salesforce Created a High-Impact Data Science Organization
#96 GPT-3 and our AI-Powered Future
#95 How to Build a Data Science Team from Scratch
#94 How Data Science Enables Better Decisions at Merck
#93 How Data Science Drives Value for Finance Teams
#92 Democratizing Data in Large Enterprises
#91 Building a Holistic Data Science Function at New York Life Insurance
#90 How Data Science is Transforming the Healthcare Industry
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