Jun Wu, Towards Data Science – April 29, 2020
As companies, particularly large organizations, scale up their models as a part of building an enterprise-wide pipeline, there’s an increasing need to operationalize the model development process. Similar to DevOps, models need to be developed, integrated, deployed and monitored. Often, with Enterprise AI initiatives, there are a host of governance considerations such as data integrity, change management, regulatory concerns, etc..
You want to be able to connect your data science pipeline to the IT organization. You want your IT organization to maintain and upgrade your data science pipeline as needed.
In other words, you want your pipeline to be fully functional across your organization, in real-time, and in line with your industry’s regulation.
