Sercompe Business Technology provides essential cloud services to roughly 60 corporate clients, supporting a total of about 50,000 users. So, it’s crucial that the Joinville, Brazil, company’s underlying IT infrastructure deliver reliable service with predictably high performance. But with a complex IT environment that includes more than 2,000 virtual machines and 1 petabyte—equivalent to a million gigabytes—of managed data, it was overwhelming for network administrators to sort through all the data and alerts to figure out what was going on when problems cropped up. And it was tough to ensure network and storage capacity were where they should be, or when to do the next upgrade.

To help untangle the complexity and increase its support engineers’ efficiency, Sercompe invested in an artificial intelligence operations (AIOps) platform, which uses AI to get to the root cause of problems and warn IT administrators before small issues become big ones. Now, according to cloud product manager Rafael Cardoso, the AIOps system does much of the work of managing its IT infrastructure—a major boon over the old manual methods.

“Figuring out when I needed more space or capacity—it was a mess before. We needed to get information from so many different points when we were planning. We never got the number correct,” says Cardoso. “Now, I have an entire view of the infrastructure and visualization from the virtual machines to the final disk in the rack.” AIOps brings visibility over the whole environment.

Before deploying the technology, Cardoso was where countless other organizations find themselves: snarled in an intricate web of IT systems, with interdependencies between layers of hardware, virtualization, middleware, and finally, applications. Any disruption or downtime could lead to tedious manual troubleshooting, and ultimately, a negative impact on business: a website that won’t function, for example, and irate customers.

AIOps platforms help IT managers master the task of automating IT operations by using AI to deliver quick intelligence about how the infrastructure is doing—areas that are humming along versus places that are in danger of triggering a downtime event. Credit for coining the term AIOps in 2016 goes to Gartner: it’s a broad category of tools designed to overcome the limitations of traditional monitoring tools. The platforms use self-learning algorithms to automate routine tasks and understand the behavior of the systems they monitor. They pull insights from performance data to identify and monitor irregular behavior on IT infrastructure and applications.

Market research company BCC Research estimates the global market for AIOps to balloon from $3 billion in 2021 to $9.4 billion by 2026, at a compound annual growth rate of 26%.1 Gartner analysts write in their April “Market Guide for AIOps Platforms” that the increasing rate of AIOps adoption is being driven by digital business transformation and the need to move from reactive responses to infrastructure issues to proactive actions.

“With data volumes reaching or exceeding gigabytes per minute across a dozen or more different domains, it is no longer possible for a human to analyze the data manually,” the Gartner analysts write. Applying AI in a systematic way speeds insights and enables proactivity.

According to Mark Esposito, chief learning officer at automation technology company Nexus FrontierTech, the term “AIOps” evolved from “DevOps”—the software engineering culture and practice that aims to integrate software development and operations. “The idea is to advocate automation

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By: MIT Technology Review Insights
Title: The AI promise: Put IT on autopilot
Sourced From:
Published Date: Mon, 28 Feb 2022 17:00:00 +0000

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