Splunk’s observability strategy has always put AI functionality at the centre. We have always recognised that, in order to make actionable sense of full fidelity data metric, event, log, and trace data streams, human cognition requires an automated assist which is precisely what AI brings to the table. As a result, throughout our observability portfolio, customers will find a variety of machine learning and pattern discovery algorithms being put to work, separating signals from noise, surfacing patterns of correlation, diagnosing root causes, and enabling remedial responses to incidents. AI, itself, is, of course, evolving at a rapid clip and with AI Assist, Splunk adds Generative or linguistic AI functionality to the mix. But what is linguistic AI and how does it relate to the non-linguistic or Foundational AI that Splunk has deployed in its products to date?

Application Performance Monitoring (APM) as a discipline and as a collection of supporting technologies has evolved rapidly since a distinct recognisable market for APM products first emerged in the 2007 - 2008 time frame. While there are many who would argue that APM has mutated into or been replaced by Observability, it makes more sense to see APM as one of many possible use cases now able to exploit the functionalities that Observability brings to the table - particularly when combined with AI.

Let's take a look at the general relationship between Observability and ML and how ML can aid in the conversion of raw telemetry into genuine signals of events or state changes.

When considering the management of telecommunications networks, it might make sense to directly consider the observable data streams available to the practitioner instead of a model-based approach. Learn more in this blog.

In the second part of this blog we will explore how increased entropy forces us to rethink what is required for monitoring.

Observability first emerged as a focal point of interest in the DevOps community in the 2017 time-frame. Aware that business was demanding highly adaptable digital environments, DevOps professionals realised that high adaptability required a new approach to IT architecture.