Automating Operations with ML

Engineers have been attracted to the idea of using Machine Learning to control their applications and infrastructure. Unfortunately, the majority of proposed uses of ML for production engineering are unsuited for the stated purpose. They generally fail to account for several structural limitations of the proposed application, including failure to account for error rate, cost versus failure and most generally insufficient number of labeled examples.

We will review the common proposed applications of Machine Learning to production control including: anomaly detection, monitoring/alerting, capacity prediction, security, and resource scaling. For each we will use experience to demonstrate the limitations that ML modeling techniques have. We will identify one application with the best results.

We will end with specific recommendations for how organizations can get ready to take advantage of ML for their production operations in the future.