Today, MLOps offers a fairly robust framework for operationalizing AI, says Zuccarelli, who's now innovation data scientist at CVS Health. By way of example, Zuccarelli points to a project he worked on previously to create an app that would predict adverse outcomes, such as hospital readmission or disease progression. That meant creating a mobile app that was reliable, fast, and stable, with a machine learning system on the back end connected via API. As MLOps platforms mature, they accelerate the entire model development process because companies don't have to reinvent the wheel with every project, he says. And this means developing expertise in a wide range of activities, says Meagan Gentry, national practice manager for the AI team at Insight, a Tempe-based technology consulting company.