How does your DevOps team apply AIOps to the data your CI/CD toolchain churns out?
While the line between DevOps and AIOps often seems blurred, the two disciplines aren’t synonymous. Instead, they present some key differences in terms of required skill sets and tools.
Artificial intelligence for IT operations, or AIOps, applies AI to data-intensive and repetitive tasks across the continuous integration and development toolchain.
DevOps professionals cite monitoring, task automation and CI/CD pipelines as prime areas for AIOps tools, but there’s still a lack of clarity around when and how broadly teams should apply AI practices.
Where AI meets IT ops
The terms AIOps and DevOps are both common in product marketing, but they’re not always used accurately. DevOps is driving a cultural shift in how organizations are structured, said Andreas Grabner, DevOps activist at Dynatrace, an application performance management company based in Waltham, Mass.
AIOps tools enable an IT organization’s traditional development, test and operations teams to evolve into internal service providers to meet the current and future digital requirements of their customers — the organization’s employees.
AIOps platforms can also help enterprises monitor data across hybrid architectures that span legacy and cloud platforms, Grabner said. These complex IT environments demand new tools and technologies, which both require and generate more data. Organizations need a new approach to capture and manage that data throughout the toolchain — which, in turn, drives the need for AIOps tools and platforms.
AIOps can also be perceived as a layer that runs on top of DevOps tools and processes, said Darren Chait, COO and co-founder of Hugo, a provider of team collaboration tools based in San Francisco. Organizations that want to streamline data-intensive, manual and repetitive tasks — such as ticketing — are good candidates for an AIOps platform proof-of-concept project.
In addition, AIOps tools offer more sophisticated monitoring capabilities than other software in the modern DevOps stack. AIOps tools, for example, monitor any changes in data that might have a significant effect on the business, such as those related to performance and infrastructure configuration drift. That said, AIOps tools might be unnecessary for simple monitoring requirements that are linear and straightforward.
The line between AIOps and DevOps
DevOps and AIOps tools are both useful in CI/CD pipelines and for production operations tasks, such as monitoring, systems diagnosis and incident remediation. But while there is some overlap between them, each of these tool sets is unique in its requirements for effective implementation. For example, AIOps automates machine language model training to complete a software build. AIOps tools must be adaptive to machine-learning-specific workflows that can handle recursion to support continuous machine language model training.
The AIOps automation design approach is fundamentally different from the repetition of the machine language training process: It’s recursive and conditional in nature, largely dependent upon the accuracy rating of procured data. The design approach also depends on selective data-extraction algorithms.
In terms of tool sets, DevOps engineers see Jenkins, CircleCI, Travis, Spinnaker and Jenkins X as CI/CD industry standards, but they aren’t AIOps-ready like tools such as Argo — at least not yet.
So, while AIOps augments DevOps with machine learning technology, AIOps isn’t the new DevOps — and ops teams should ignore the hype that tells them otherwise.
This was last published in January 2019