Intelligent automation and the use of Machine Learning to make sense of the big data generated through pipelines, software-defined networks, infrastructure, and millions of containers which are run in production every day. Use cases are automated governance and change management, root cause analysis, anomaly detection, event correlation, incident prevention, and reducing the mean time to repair (MTTR) for incidents.
Very simple use cases are opening a ticket automatically, and assigning it to the correct team when certain patterns are observed in production
across multiple monitoring tools at different levels of application, pipeline, network, and infrastructure
with higher accuracy for ruling out false alarms.
More complicated use cases are assessing the risk of a change to established automated governance and change management across software delivery pipelines, and also defining and assessing the quality of the change. And the next level is auto-healing of our systems by applying automated patches based on past remediations and how effective they have been.