For AI rollouts, hazards reported ahead: Getting machine learning to work is made a lot harder by HR and IT issues - [Spectral Lines]

Implementing machine learning in the real world isn't easy. The tools are available and the road is well marked—but the speed bumps are many. ¶ That was the conclusion of panelists at the IEEE AI Symposium 2019, held at Cisco's San Jose, Calif., campus in September. _ The toughest problem, says Ben Irving, senior manager of Cisco's strategy innovations group, is people. ¶ It's tough to find applicants with expertise in data science, he indicated, so companies are looking into nontraditional sources of personnel, like political science. "There are some untapped areas with a lot of untapped data-science expertise," Irving says. ¶ Lazard managing director Trevor Mottl agreed that would-be data scientists don't need formal training or experience to break in. "This field is changing really rapidly," he says. "There are new language models coming out every month, and new tools, so [anyone should] expect to not know everything. Experiment, try out new tools and techniques, read; there aren't any true experts at this point because the foundational elements are shifting so rapidly."¶"It is a wonderful time to get into a field," he said, noting that it doesn't take long to catch up "because there aren't 20 years of history."¶ Confusion about what different kinds of machine-learning specialists do doesn't help the personnel situation. An audience member asked panelists to explain the difference between a data scientist, a data analyst, and a data engineer. Darrin Johnson, Nvidia global director of technical marketing for enterprise, admitted it's hard to sort out, and any two companies could define the positions differently. ¶ The competition to hire data scientists, analysts, engineers, or whatever companies call them requires that managers make sure that any work being done is structured and comprehensible at all times, the panelists cautioned.¶ "We need to remember that our data scientists go home every day and sometimes they don't come back, because they go home and then go to a different company," says Lazard's Mottl. "If you give people a choice on [how they do their development] and have a successful person who gets poached by a competitor, you have to either hire a team to unwrap what that person built or jettison their work and rebuild it."