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.
Successful machine learning deployment requires a specialized business practice for broad adoption.
Empowering stakeholders with key semi-technical knowledge enhances collaboration and project success.
Aligning technical metrics with business needs is crucial for effective machine learning deployment.
Deep dives
Machine Learning Deployment Challenges
Deploying machine learning models poses significant challenges as many initiatives fail to reach deployment, resulting in unrealized value. Specialized business practices are necessary for broad adoption
Value of Semi-Technical Understanding
The importance of a semi-technical understanding for both business and data professionals is highlighted to facilitate collaboration in machine learning projects. Empowering stakeholders with key knowledge enhances project success.
Predictive Analytics and Business Metrics
Emphasizing the shift towards utilizing predictive analytics for measuring business impacts through relevant metrics like revenue, profit, and returns. Aligning technical metrics with business needs is crucial for effective deployment.
Organizing Around Predictive Use Cases
Proposing the organization of companies around predictive use cases to streamline deployment processes. Planning projects around predictive technology from inception ensures seamless integration with existing operations.
Educational Needs for Machine Learning Projects
Addressing the need for educational initiatives focused on non-technical stakeholders and data scientists to bridge the gap in understanding. Enhancing business professionals' understanding of machine learning concepts and value can significantly impact project outcomes.
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.