This article provides a glimpse into how intelligent observability works with telemetry data to add context and insights for DevOps practitioners and SRE teams.
There’s an AI-led developer and operations (DevOps) evolution afoot which is stoking SREs’ increasingly critical efforts to assure and improve the customer experience by automating the toil out of observability.
This movement feeds on a supercharged process of turning telemetry into actionable insight by automatically drawing anomalies, changes and events out of the full-stack event and telemetry data, and analyzing it for correlation and causality. In a fully digital economy, a movement like this puts SREs in the driver’s seat of not just development, but of an organization’s entire success.
This was the theme of a recent All Day DevOps breakout session,
During this talk, Frank shined a light on both the critical nature that IT monitoring plays in determining how fast an organization can innovate, and just how AI helps DevOps practitioners and SRE teams do this faster. Ultimately, the talk showcases how AI and observability together help these teams move fast while breaking things less.
As SREs and people practicing DevOps, we want to create a continuous learning cycle to build more reliability from the knowledge we obtain about our customers’ experience,” Frank told the global audience. “This knowledge will resolve incidents before there’s business impact by helping SREs to see what could happen before it actually happens.
Gone are the days when operations and development were a back-office affair. Frank compared the role of today’s SREs to that of an astronaut — one of the more high-stakes roles a human can operate in. In both cases, he said, staying calm in high-stress and high-stakes situations can best be done by having the right knowledge.
But deciphering actionable insights from data can often be the biggest challenge, he said. This is where mathematical processes step in to help SREs and DevOps practitioners bring the data from its onset of little context to the mega context needed. This automated analysis of telemetry, Frank said, brings teams closer to self-optimization and closed-loop remediation throughout cycles and software pipelines.
“You can get the knowledge you need applying AI to your observability data, automating monitoring practices and surfacing actionable information to improve the customer experience, automating every step of the way from creating the data to letting us know what we actually need to do,”
“Effectively taking the mountains of data down to actionable information.”
Frank proceeded to outline the steps that intelligent observability takes to turn telemetry data into actionable insights. From utilizing a robust measure of the variability of the univariate sample of quantitative data to calculating the distribution of priority of an event, he outlined the measurements that intelligent observability takes at machine speed to identify anomalies and probable root causes.
In conclusion, Frank stressed that the time has come to abandon manually monitoring observability data from metrics, logs and traces.
“Creating static thresholds to try to deal with the vast amounts of data will cause burnout,”
“You will continue to receive multiple alerts from disparate systems at 3:30 am waking you up expecting you to run ad-hoc queries to decipher your own context from multiple dashboards. The crucial and only way to make sense of the data, reduce toil, and improve productivity and the value you are delivering is to apply multiple layers of AI — because observability and AI are better together.”
Moogsoft is the AI-driven observability leader that provides intelligent monitoring solutions for smart DevOps. Moogsoft delivers the most advanced cloud-native, self-service platform for software engineers, developers, and operators to instantly see everything, know what’s wrong and fix things faster.
This article is first published here: https://www.moogsoft.com/blog/how-observability-and-ai-work/