Defining and embracing ModelOps: Scaling up data science

Traditionally, data science teams are full of brilliant people working solo, tapping into their own data sources, running things on behalf of a department, and not the entire business. But a transition is afoot, and it can be seen in the adoption of ModelOps, driven in part by the need for data science teams to be more sophisticated and grown-up, combined with the desire for better, reusable analytics frameworks, that leverage the power of a group versus the power of an...