A Use Case:
Machine Learning (ML) is a dynamic field where models are continuously improved and updated. Consider an ML engineer at a tech company that deploys models for image recognition. Every time the engineer updates or improves a model, they must ensure it meets the required accuracy and performance metrics before it’s deployed into production.
Manually testing these models every time they’re updated can be tedious and error-prone. Moreover, waiting for scheduled tests may delay the deployment of an improved model, which could mean missing out on enhanced performance or user experience.