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William Cappelli
William Cappelli

Willam Cappelli is an Observability Strategist at Splunk. Widely recognised as a key interpreter and shaper of software market and technology trends, William wrote the first papers defining AIOps, APM, and DEM while serving as a VP of Research at Gartner and then, as a CTO at Moogsoft developed the broadly accepted five dimensional model for AIOps. In his spare time, he studies and translates Dharma language texts.

DevOps 9 Min Read

Why Lingusitic and non-Linguistic AI are Complementary

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?
DevOps 11 Min Read

Don’t Live in the Past - APM 3.0 and Why You Need It

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 While there are many who would argue that APM has mutated into or been replaced by Observability, it makes more sense to see APM as one of many possible use cases now able to exploit the functionalities that Observability brings to the table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.
DevOps 4 Min Read

Observability and Machine Learning [Part 1]

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.
DevOps 6 Min Read

Observability and Telecommunications Network Management [Part 1]

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.
DevOps 5 Min Read

The Importance of Traces for Modern APM [Part 2]

In the second part of this blog we will explore how increased entropy forces us to rethink what is required for monitoring.
DevOps 6 Min Read

Observability Shifts Right

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.