Capitalizing on this technology is criticalbut its notoriously difficult to launch. Many ML projects never progress beyond the modeling: the number-crunching phase. Industry surveys repeatedly show that most new ML initiatives dont make it to deployment, where the value would be realized.
Hype contributes to this problem. ML is mythologized, misconstrued as intelligent when it is not. Its also mismeasured as highly accurate, even when that notion is irrelevant and misleading. For now, these adulations largely drown out the words of consternation, but those words are bound to increase in volume.
Take self-driving cars. In the most publicly visible cautionary tale about ML hype, overzealous promises have led to slamming on the brakes and slowing progress. AsThe Guardianput it, The driverless car revolution has stalled. This is a shame, as the concept promises greatness. Someday, it will prove to be a revolutionary application of ML that greatly reduces traffic fatalities. This will require a lengthy transformation that is going to happen over 30 years and possibly longer, according Chris Urmson, formerly the CTO of Googles self-driving team and now the CEO of Aurora, which bought out Ubers self-driving unit. But in the mid-2010s, the investment and fanatical hype, including grandiose tweets by Tesla CEO Elon Musk, reached a premature fever pitch. The advent of truly impressive driver assistance capabilities were branded as Full Self-Driving and advertised as being on the brink of widespread, completely autonomous drivingthat is, self-driving that allows you to nap in the back seat.
Expectations grew, followed by . . . a conspicuous absence of self-driving cars. Disenchantment took hold and by the early 2020s investments had dried up considerably. Self-driving is doomed to be this decades jetpack.
What went wrong? Underplanning is an understatement. It wasnt so much a matter of overselling ML itself, that is, of exaggerating how well predictive models can, for example, identify pedestrians and stop signs. Instead, the greater problem was the dramatic downplaying of deployment complexity. Only a comprehensive, deliberate plan could possibly manage the inevitable string of impediments that arise while slowly releasing such vehicles into the world. After all, were talking about ML models autonomously navigating large, heavy objects through the midst of our crowded cities! One tech journalist poignantly dubbed them self-driving bullets. When it comes to operationalizing ML, autonomous driving is literally where the rubber hits the road. More than any other ML initiative, it demands a shrewd, incremental deployment plan that doesnt promise unrealistic timelines.
The ML industry has nailed the development of potentially valuable models, but not their deployment. A report prepared by theAI Journalbased on surveys by Sapio Research showed that the top pain point for data teams is Delivering business impact now through AI. Ninety-six percent of those surveyed checked that box. That challenge beat out a long list of broader data issues outside the scope of AI per se, including data security, regulatory compliance, and various technical and infrastructure challenges. But when presented with a model, business leaders refuse to deploy. They just say no. The disappointed data scientist is left wondering, You cant . . . or you wont? Its a mixture of both, according to a question asked by my survey with KDnuggets (see responsesto the question, What is the main impediment to model deployment?). Technical hurdles mean that they cant. A lack of approvalincluding when decision makers dont consider model performance strong enough or when there are privacy or legal issuesmeans that theywont.
Another survey also told this some cant and some wont story. After ML consultancy Rexer Analytics survey of data scientists asked why models intended for deployment dont get there, founder Karl Rexer told me that respondents wrote in two main reasons: The organization lacks the proper infrastructure needed for deployment and People in the organization dont understand the value of ML.
Unsurprisingly, the latter group of data scientiststhe wonts rather than the cantssound the most frustrated, Karl says.
Whether they cant or they wont, the lack of a well-established business practice is almost always to blame. Technical challenges abound for deployment, but they dont stand in the way so long as project leaders anticipate and plan for them. With a plan that provides the time and resources needed to handle model implementationsometimes, major constructiondeployment will proceed. Ultimately, its not so much that they cant but that they wont.
About the Author
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-runningMachine Learning Weekconference 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 ofThe Machine Learning Times, and afrequent keynote speaker. He wrote the bestsellingPredictive Analytics: The Power to PredictWho Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities, as well asThe AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Erics interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduatecomputer sciencecourses in ML and AI. Later, he served as abusiness schoolprofessor at UVA Darden. Eric also publishesop-eds on analytics and social justice.
Eric hasappeared onBloomberg TV and Radio, BNN (Canada), Israel National Radio, National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet. Eric and his books have beenfeatured inBig Think, Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, Fast Company, The Financial Times, Forbes, Fortune, GQ, Harvard Business Review, The Huffington Post, The Los Angeles Times, Luckbox Magazine, MIT Sloan Management Review, The New York Review of Books, The New York Times, Newsweek, Quartz, Salon, The San Francisco Chronicle, Scientific American, The Seattle Post-Intelligencer, Trailblazers with Walter Isaacson, The Wall Street Journal, The Washington Post,andWSJ MarketWatch.
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Effective Machine Learning Needs Leadership Not AI Hype - The Machine Learning Times
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