We were exchanging emails with the APIMetrics team this week, discussing the challenges that come up when monitoring APIs. We are working through the different ways in which we can create real-time streams of data from monitors that APImetrics has set up, and one of the challenges you encounter when monitoring API infrastructure at scale is the ability to sift through large volumes of signals to understand what truly matters. When you are monitoring a large number of APIs, depending on the approach, the overall health of the API, and the clients that are consuming resources, you are likely to experience regular waves of HTTP status codes to sort through looking for the responses that actually matter and mean something. We are thinking about how cutting HTTP status code noise will make API monitoring streams more intelligent.
As we were discussing this, the topic of machine learning (ML) came up, and how we can use it to help make sense of the potentially huge amounts of HTTP status codes we’ll have to process. Allowing us to not respond to individual signals, but groups or patterns of signals, and make API monitoring a little more intelligent, and reduce the chances we’ll miss some important monitoring updates. Using machine learning to do the heavy lifting of looking through large amounts of data, and rely on humans to do the responding when relevant patterns are uncovered. If you rely on humans to look at every signal returned they are inevitably going to burn out, miss patterns, and become blind to much of what is returned. By leaning on ML to look through the volume of data, we can lighten the load for the human, and help them be a little more efficient in what they deliver. https://goo.gl/cXToqw #DataIntegration #ML
