Monitoring the performance of Machine Learning (ML) models is critical to gain insights, anticipating trends, and effectively managing ML operations. With ML monitoring, you’ll have access to metrics that let you track changes to performance over time and data about input sources for debugging and diagnostics when things go wrong.
It’s about taking a proactive rather than reactive approach—ensuring that ML pipelines are running as intended and providing value-driven results you can rely on.
Model drift, also known as data drift, is an essential element of machine learning (ML) monitoring. This phenomenon occurs when models become increasingly out-of-sync with their training data and cause cascading errors that can threaten the reliability of a system or even an organization’s operations.