Summary

This chapter was a deep dive into AIOps. This is a rather new domain, but very promising. We've learned how AIOps platforms are built and learn as they are implemented in enterprises. It's important to understand that you need a logical architecture to have a complete overview of how systems fulfill functionality and how they are related to other systems, without already knowing the full technical details of these systems.

Next, we defined the key components of AIOps, being big data and machine or deep learning. AI only performs if it has access to enough relevant data on which it can execute analytic models. These models will teach the platform how to detect issues, anomalies, and other events, predict the impact on the IT landscape, find root causes faster, and eventually trigger actions. These actions can be automated. AIOps platforms will avoid a lot of tedious, repetitive work for operators, something that is called toil in SRE.

We've learned what...