Observability and AI Partner Well to Tackle All of That Data | HackerNoon

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,”Frank proceeded to outline the steps that intelligent observability takes to turn telemetry data into actionable insights.

In conclusion, Frank stressed that the time has come to abandon manually monitoring observability data from metrics, logs and traces.he said. “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.

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