A Solution for Monitoring Image Data

The article discusses the challenges of monitoring image data within machine learning ecosystems. It highlights that maintaining observability is crucial as data volumes grow and complexities increase. The article suggests that monitoring unstructured data such as images can be achieved by capturing structured telemetry, which is compatible with common statistical approaches. It also mentions various physical factors like device settings, changes in environment, and object detection that can impact the consistency and quality of image data. Furthermore, it discusses data pipeline factors like swapped color channels, inconsistent color spaces, and scaling issues that can introduce points of failure. The article proposes a solution by computing metrics sensitive to these events such as mean pixel value for brightness, hue and saturation for color palette, and image height and channel count for colorspace. It also mentions the use of Exif data for additional information like geolocation. Finally, it introduces whylogs, an open-source data logging library designed to capture valuable telemetry in a customizable way for any dataset, which can be used with powerful anomaly detection, informative visualizations, and automated notifications through the WhyLabs AI Observatory.