Operationalizing machine learning: The future of practical AI

The key to delivering consistent business value with AI is to employ operational machine learning workflows that fully integrate machine learning models into standard enterprise processes in a reliable and repeatable fashion. That's where MLOps comes in. "There are fundamentally two things enterprises can do with machine learning: One is to make processes more efficient, and the other is to launch new products and features," says Piero Cinquegrana, data scientist and co-author of O'Reilly's "Machine Learning at Enterprise Scale." These processes could be sales process, marketing measurement, operations, and tasks that are repeatable and automatable--all kinds of what Cinquegrana calls domains. "Some classic use cases are measurement, such as scoring leads for sales so that sales account executives don't have to cold call a long list of unqualified leads," he says.