Monitoring active ML pipelines
The proactive monitoring of active ML pipelines is critical to ensure their optimal performance in production environments. Achieving this requires a focused approach on several key areas for effective observation, utilizing a variety of specialized tools specifically designed for these tasks. A central aspect of this monitoring process is comprehensive logging. It is essential for every phase of the active ML pipeline to implement detailed logging practices, capturing a broad spectrum of data, such as useful insights, errors, warnings, and other pertinent metadata. This diligent approach to log monitoring is key in quickly identifying and diagnosing issues, enabling prompt and efficient resolutions. Furthermore, these logs offer invaluable insights into the pipeline’s performance and behavior, aiding in the continuous enhancement of the active ML systems. Simple logging can be done in the scripts themselves with libraries such as logging, which...
