10 Key Roles For AI Success - AI Summary

"This person is tasked with packing the ML model into a container and deploying to production -- usually as a microservice," says Dattaraj Rao, innovation and R&D architect at technology services company Persistent Systems. The role requires expert back-end programming and server configuration skills, as well as knowledge of containers and continuous integration and delivery deployment, Rao says. They are crucial to AI initiatives because data needs to be both collected and made suitable for consumption before anything trustworthy can be done with it, says Erik Gfesser, director and chief architect at Deloitte. This person is an authority in their domain, can judge the quality of available data, and can communicate with the intended business users of an AI project to make sure it has real-world value. When Babych's company developed a computer-vision system to identify moving objects for autopilots as an alternative to LIDAR, they started the project without a domain expert.