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CI/CD / Observability

The Big Picture of AIOps: Why You Need AI to Take Over DevOps

In this post — in addition to defining and describing AIOps — we cover how AIOps methodology factors into DevOps and offers new advantages over traditional approaches. We also look at AIOps culture and what enterprise IT teams often get wrong when starting on an AIOps journey.
Sep 4th, 2019 9:27am by
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Dynatrace sponsored this post.

Andreas Grabner
Andreas is a DevOps Activist at Dynatrace. He has over 20 years of experience as a software developer, tester and architect, and is an advocate for high-performing cloud operations. As a champion of DevOps initiatives, Andreas is dedicated to helping developers, testers and operations teams become more efficient in their jobs with Dynatrace’s software intelligence platform.

As enterprise cloud environments are getting more complex, AI and automation have become increasingly seen as the solutions for managing and streamlining an issue that’s grown beyond what humans can handle.

I’ve talked in previous articles about how AI is helping to usher in an enterprise IT culture change, shifting IT Ops and DevOps to AIOps. But in order to understand how that works, it is necessary to know what AIOps is in the first place.

In this post — in addition to defining and describing AIOps — we cover how AIOps methodology factors into DevOps and offers new advantages over traditional approaches. We also look at AIOps culture and what enterprise IT teams often get wrong when starting on an AIOps journey.

Defining AIOps

Today’s enterprise cloud environments are rife with complexity. They often consist of highly dynamic, web-scale and multicloud environments, with containers and microservices — and they have become impossible to manage manually anymore.

Look at it from an IT Ops perspective: How do you correlate system events with root causes when there are billions of dependencies between applications, clouds, infrastructure and microservices to keep track of? How do you nip these issues in the bud before they impact the end-user if you don’t know what they are, or where they’re coming from, until after the fact? How can you truly remediate issues if you don’t know the root cause? And how can IT manually interpret the volume of complex data in modern cloud environments to find the answers — not just more data — to these challenges?

They can’t. And that’s where AIOps enters the picture.

AIOps marries AI and automation to IT Ops, automating manual configurations, streamlining cloud environment complexity, providing quicker solutions to reducing alert noise and finding root-cause answers. More than that, though, AIOps is all about building a culture of trust around AI. In other words, trusting AI to alert you to real problems impacting end-users or your SLAs, and linking them to root causes.

That, in turn, also means you can trust it to automate traditional IT Ops tasks such as restarting services, changing configurations or scaling dynamic resources to what is needed. And the more IT can trust AI to automate their usual business functions, the more time they have to spend on innovating and delivering new products faster than before.

The Hard Way to Do AIOps

Alert storms and the sheer quantity of never-ending events bombarding IT is overwhelming; we’ve reached the tipping point where humans can no longer keep up. But it’s a mistake to think AIOps is all about cutting down on this blizzard of system alerts generated by traditional monitoring tools. I’ve seen enterprises go the route of layering machine learning (ML) algorithms on top of their monitoring tools, sending all the alerts from those tools into a big data platform and then using the ML algorithms to identify precise, actionable insights. Fewer alerts mean problem solved, right?

While that might sound like a logical way of doing AIOps, it also makes life unnecessarily harder for IT. Because even with the best ML, the basic equation remains the same: the more unpredictable data you bring in, the more unpredictable results you get out of it. For instance, if your monitoring tools are registering all green lights while end-users are still seeing system problems, then stacking ML on top of that won’t solve your real issue. It may cut down the number of alerts you’re getting, but only because it’s interpreting false green lights as real green lights and not accurately reflecting what’s happening under the hood.

Legacy monitoring had its time when the technologies that were monitored were static and predictable. But because legacy monitoring can’t keep up with dynamic enterprise cloud environments today, alerting schemes remain out of date, resulting in that intake of unpredictable data. Not to mention the massive manual efforts needed to tune, manage and configure the alerting rules for monitoring tools that ML can’t do. If a solution requires that many manual inputs, it defeats the purpose of automating it in the first place.

Deterministic AI Is AIOps Done the Easy Way

Getting the best answers with AI means having the best data. The easier way to execute an AIOps solution that gets the best data, and the best answers from it, is by deploying a deterministic AI system that can ingest data with relational context from different APIs across the full tech stack — from infrastructure to end-users, from the CI/CD pipeline to ITSM tools to the cloud.

Deterministic AI differs from the traditional ML approach because it doesn’t primarily draw on data provided by different tools monitoring different layers of your stack in isolation. Instead, it captures five distinct dimensions of data — topology, traces, metrics, logs and events — by connecting that data to context. That context is drawn from distributed tracing of horizontal dependencies (i.e. end-to-end service traces) and full-stack dependency detection of vertical dependencies (i.e. from host to end-user) and tells the AI what the true relationship between data elements is at any moment in time, without having to use a statistical approach that, in a best-case scenario, just comes up with a “good guess.”

It’s this level of context that enables a deterministic AI approach to work fast and reliably in analyzing data, telling you who are impacted and what the root cause of an issue may be, then auto-triggering specific remediation actions. All of which then feeds into creating an AIOps culture that facilitates more of these automated, high-fidelity data-backed solutions.

AIOps Is Reimagining the Role of IT Ops

AIOps isn’t just about streamlining cloud complexity and providing faster, more precise solutions — it’s changing the role that operations engineers play in IT. Instead of spending hours staring at dashboards, Ops engineers are freed to become engineers with developer skills, and use those skills to mentor their development teams. That gives IT Ops the opportunity to become more of a hybrid cloud service Ops team that can provide the business with a powerful platform for deploying and operating applications and services in a fully automated and self-service fashion.

This is the ultimate knock-on benefit of AIOps: It’s not just about using AI to make complex enterprise cloud environments easier to manager, but about empowering IT Ops engineers to provide internal hybrid-cloud platform services with AI at the core. IT Ops engineers become mentors and provide AI-supported delivery pipelines that, in turn, help developers and the business to generate new products and new innovations faster than would otherwise be possible.

Feature image via Pixabay.

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