Using ML to Automate Dynamic Error Categorization

Logs analysis and information extraction in highly dynamic production environments is a complicated task. This talk will present how we designed a platform that, by leveraging unsupervised machine learning techniques, is able to dynamically categorize errors on logs generated by several micro services in our provisioning pipeline. It will focus not only on how is it important to select the most appropriate clustering algorithm, but equally on how is fundamental to invest in production services with well defined logging.