News

New Relic Updates AIOps Capabilities

• By John K. Waters
• 03/31/2021

New Relic has announced an update to its New Relic One observability platform with enhancements to its AI- and ML-based on-call DevOps capabilities (AIOps).

Part of the New Relic Applied Intelligence suite within the platform, and enabled by default to all users of those tools, the AIOps features are designed to help site reliability engineering (SRE) and network operations center (NOC) teams detect, understand, and resolve incidents faster.

The AIOps capabilities in the platform allow engineers to spot anomalies automatically based on "golden signals," such as throughput, errors, and latency across all applications, services, and log data; avoid "alert storms" across multiple tools by auto-correlating events based on time, context from alert messages, and now relationship data across systems; and use "automated insights" into the probable root cause for incidents.

There's also support for the detection of patterns and outliers in log data and integration with PagerDuty and other popular incident management tools.

New Relic's AIOps features combine applied intelligence and ML technologies to provide intelligence and automation that augments the capabilities of existing incident management teams and workflows to get closer to root causes faster, the company said.

New Relic introduced its AIOps capabilities last year, and integrated them into with the New Relic One platform.

“AIOps has promised engineers the ability to harness AI and machine learning to predict possible issues, determine root causes, and intelligently drive automation to resolve them,” said Bill Staples, New Relic president and chief product officer, in a statement. “Despite the hype, many DevOps and SRE teams have struggled to achieve the value of AIOps, as steep learning curves, long implementation and training times, prohibitive pricing, and lack of confidence in AI and machine learning have stood in the way…. New Relic is solving these challenges, putting the power of observability in the hands of every engineer to finally deliver the promised value of AIOps to everyone.

Gartner coined the term "AIOps" in 2016 to categorize a set of machine learning analytics technologies that enhance IT operations. In a 2019 report ("Artificial Intelligence for IT Operations Delivers Improved Business Outcomes"), Gartner analyst Charley Rich wrote: "AIOps will detect patterns a human would be unlikely to uncover, including those that reveal cause and effect."