Eric Siegel, leading consultant and former Columbia University professor, discusses his book 'The AI Playbook' on mastering machine learning deployment. He highlights the challenges in deploying machine learning projects and the importance of a specialized business practice. Siegel shares success and failure stories, emphasizing collaboration between business and data professionals for project success.
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volunteer_activism ADVICE
Six-Step Playbook
Use a structured six-step process for machine learning projects.
This approach ensures successful deployment and value realization, benefiting both business and data professionals.
insights INSIGHT
Data's Role in Prediction
Businesses need prediction, which requires machine learning, which in turn requires data.
This data-driven prediction powers major operations, from cost reduction to election outcomes.
question_answer ANECDOTE
Micro-Risk Management
Eric Siegel uses his skiing accident and subsequent insurance claim to illustrate micro-risk management.
This concept parallels predictive analytics, where probabilities assess potential negative outcomes.
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Leading consultant and former Columbia University professor Eric Siegel visits Google to discuss his book “The AI Playbook: Mastering the Rare Art of Machine Learning Deployment.” The book explains how machine learning works and how to successfully operationalize it.
The greatest tools are often the hardest to use. Machine learning is the world’s most important general-purpose technology – but it’s notoriously difficult to launch. Outside Big Tech and a handful of other leading companies, machine learning initiatives routinely fail to deploy, never realizing value. What’s missing? A specialized business practice suitable for wide adoption. In "The AI Playbook", bestselling author Eric Siegel presents the gold-standard, six-step practice for ushering machine learning projects from conception to deployment. He illustrates the practice with stories of success and of failure, including revealing case studies from UPS, FICO, and prominent dot-coms. This disciplined approach serves both sides: It empowers business professionals and it establishes a sorely needed strategic framework for data professionals.
Beyond detailing the practice, this book also painlessly upskills business professionals. It delivers a vital yet friendly dose of semi-technical background knowledge that all stakeholders need in order to lead or participate in machine learning projects. This puts business and data professionals on the same page so that they can collaborate deeply, jointly establishing precisely what machine learning is called upon to predict, how well it predicts, and how its predictions are acted upon to improve operations. These essentials make or break each initiative – getting them right paves the way for machine learning’s value-driven deployment.
Talks at Google