Vivek Kumar, Analytics Insight – June 29, 2020
ModelOps (model operations) generally refers to the development of an analytics model that moves from the lab to IT production. It is a DevOps variation and follows DevOps principles to ensure IT compliance, security and manageability. As DevOps is designed to focus on application development, ModelOps focuses on analytics. This is vital for predictive analytics, allowing the continuous delivery, and efficient development and deployment of models.
While a large number of organizations are relying on machine learning models to derive fresh insights and information from voluminous data, SAS noted, these machine learning models are not limited by the number of data dimensions they can effectively access and use vast amounts of unstructured data to identify patterns for predictive purposes. But model development and deployment are not an easy task. Only about 50 percent of models are ever put in production and those that are taken at least three months to be ready for deployment. This time and effort equal a real operational cost as well as mean a slower time to value.
