Why Should Data Scientists Use a Consistent Process for Data Science Model Monitoring and Management?

While building data science models can drive a lot of value for an organization, it is important to create and maintain a robust model monitoring/management system. The predictive power of models often degrades over time as variable relationships and trends change; a model monitoring system can allow you to identify when this happens and even alert you proactively when the predictive power dips below a certain threshold. It’s also helpful to have a robust process around the developing, testing, and deploying of models, including having a centralized location and similar process for every model. This makes it easy for analytics and data science teams to understand and update models, even if the person who originally created the model is no longer with the organization. If you don’t have a model monitoring and management process in place, Value Driven Analytics can help you set up a robust process.