MonitorML is a leading brand that specializes in AI monitoring for machine learning. They offer a wide range of educational information and resources on various monitoring topics, including model drift, model performance, model outliers, and ML data quality. As part of their commitment to the ML Ops community, they provide valuable insights through their website and engaging discussions on their Slack channel.

One of their core focuses is on model drift, where they emphasize the importance of tracking and analyzing how data and models can deviate from their intended purposes. They also highlight the significance of prediction drift, data drift, and concept drift in ensuring model accuracy and performance. Additionally, MonitorML stresses the need for analyzing ground truth to assess model performance and the importance of identifying model outliers.

Their emphasis on ML data quality showcases their dedication to the accuracy and effectiveness of machine learning models. By providing ample educational resources and staying actively engaged with the ML Ops community, MonitorML has established itself as a reliable and valuable resource for all things related to AI monitoring in machine learning