TechOps, standing for Technical Operations, has increasingly become an umbrella term for a number of IT specialties, including IT Operations, DevOps, and TechOps itself, but TechOps really is something altogether singular. TechOps teams are specifically there to manage servers. At multiple points along a business’s journey, there will be times when servers are stretched to their breaking point and it’s at that moment that TechOps teams are spun up and put to work.
With so much responsibility in their hands, it’s in the best interest of organizations that TechOps teams have the latest technology available. Recent strides in AI have the capability to hugely impact how TechOps departments are managed and run, overcoming daily challenges they encounter in monitoring, alerts, redundancies, and, of course, automation. Ultimately, artificial intelligence could prove to be the difference between TechOps teams that struggle and TechOps teams that fly.
If server performance is key, then monitoring is king. TechOps teams have long known that continuous monitoring of servers, applications, and networks is necessary and some have invested in tools that display data in accessible ways and trigger alerts. But continuous monitoring has long been troublesome, with TechOps teams forced to manually create and retire triggers. In the worst cases, TechOps teams have been forced to take standby shifts to address and resolve issues before they escalate.
New advances in AI-powered monitoring tools automate many of the tasks that were formerly done by TechOps teams manually. Unlike traditional tools, AI-powered tools don’t require any manual configuration and can automatically detect inactive and new components, saving TechOps teams much of the busy work of adding and removing triggers. AI-powered tools also have the capacity to detect anomalies and alert TechOps teams before there are issues, largely preventing the need for non-stop standby.
Every TechOps team worth its chops knows that setting thresholds for different alerts is the only thing standing between them and alert fatigue. While good monitoring systems have always been around to reliably send alerts based on preconfigured thresholds, AI-powered monitoring tools take it a giant step forward by sending actionable, intelligent alerts that don’t need additional correlative and contextual analysis. In other words, AI-powered monitoring tools conduct root cause analysis automatically, saving teams from needing to correlate issues manually.
TechOps teams must be constantly testing for failures and making sure that each component of the production environment can withstand them. AI-powered monitoring software can help TechOps teams create redundancies throughout the environment by automatically logging incidents and adding them to a proprietary database. With predictive capabilities, the software can also single out components that are missing redundancies and help TechOps teams proactively conduct failure testing.
TechOps is a never-ending task and a never-ending learning experience. But for all the challenges that face TechOps teams in running the backbone of the business, technology has caught up to their needs in a way that’s unprecedented and transforming. Smart tools has the ability to not just overcome four central challenges that TechOps contend with every day, but also to systematically address any and all threats to the delivery of business services. For TechOps, artificial intelligence means healthy production servers.
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Loom Systems delivers an AIOps-powered log analytics solution, Sophie,
to predict and prevent problems in the digital business. Loom collects logs and metrics from the entire IT stack, continually monitors them, and gives a heads-up when something is likely to deviate from the norm. When it does, Loom sends out an alert and
recommended resolution so DevOps and IT managers can proactively attend to the issue before anything goes down.
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