Here is an anomaly detection tutorial that i created for my boss and the open source community where i work.
It’s part of some work i have been doing around adding some anomaly detection functionality into our open source monitoring project.
Like most ML projects the trickiest, but maybe most important, part can often be educating and empowering people to actually understand a bit more than just the ML hype and buzz they read.
It can be painful and difficult (and often neglected) but once everyone involved and even relevant end users have some comfort and confidence that they actually understand enough of what’s going on and how the system might be using the data then we can all work together to make the project more likely to succeed and the ML driven parts of it just a means to an end as opposed to the end itself.
