Continuously track models in production to automatically detect drift and maintain accuracy in rapidly changing live environments
Nothing lasts forever—not even carefully constructed models that have been trained using mountains of well-labeled data. In these turbulent times of massive global change emerging from the COVID-19 crisis, ML teams need to react quickly to adapt to constantly changing patterns in real-world data. Monitoring machine learning models is a core component of MLOps to keep deployed models current and predicting with the utmost accuracy, and to ensure they deliver value long-term.