Smriti Srivastava, Analytics Insight – May 6, 2020
All models degrade, and if they are not given regular attention, performance suffers. Models are like cars: To ensure quality performance, you need to perform regular maintenance. Model performance depends not just on model construction, but also on data, fine-tuning, regular updates, and retraining.
Here ModelOps come into play.
ModelOps allows you to move models from the lab to validation, testing, and production as quickly as possible while ensuring quality results. It enables you to manage and scale models to meet demand and continuously monitor them to spot and fix early signs of degradation. ModelOps is based on long-standing DevOps principles. It’s a must-have for implementing scalable predictive analytics. But let’s be clear: Model development practices are not the same as software engineering best practices. The difference should become clearer before its implementation.
