4 Ways of Getting the Most Out of AIOps

Andreas Grabner
Learn more about Dynatrace

As organizations strive to advance digital acceleration efforts, outpace competitors, and better service customers, the path to better, more secure software lies in AIOps.

Coined by Gartner in 2016, AIOps — or AI for IT operations — has become an IT best practice only in the past few years. In short, AIOps offers developers and their DevOps and SRE teams a fast and automated solution for delivering observability (and precise insights) into their production environments at scale — making it easier for those teams to troubleshoot problems, identify root causes and remediate issues before they can impact the end-user experience or hinder the business bottom line.

Over the course of the next year, organizations expect production deployments to grow by 10x the current deployment rates, but as those deployments skyrocket, output volumes and production processes will also grow in complexity. An AIOps solution should be able to scale to meet that volume and process accordingly, but the fact is, not all can. Scalability and operational efficiency are only as effective as the AIOps solution you're leveraging.

As DevOps teams continue to adopt progressive delivery models — like Canary, Blue/Green and Feature Flags for upgrading and replacing individual services — and the volume of production deployments and configuration changes sees even more growth, here are a few of the things that your DevOps teams should keep in mind, as they look to make the most of their IT toolkits via AIOps:

1. Create test-driven operations

Bolster your AIOps' resiliency by testing auto-remediation scripts before entering production, rather than reactively.

For example, SREs can orchestrate a pre-production environment that's monitored by the AIOps solution. By loading tests and injecting chaos into this "test-driven operations" environment, and using it to validate auto-remediation scripts, your AIOps solution's capability for deploying auto-remediation code when an issue (inevitably) arises is further validated. Instead of SREs scripting and deploying code reactively (once an issue has been experienced), AIOps can deploy it proactively — fixing the issue immediately, thanks to having been "battle tested" for those scenarios in advance.

2. Push deployment/configuration data to AIOps

Linking events to a monitored entity makes it easier for AIOps to analyze and correlate behavior — necessary for going beyond simple correlations, to provide instead, more precise root cause answers. Pushing contextual deployment information (i.e., deployment, load test, load balance, configuration changes, service restart, etc.) to AIOps makes it possible to immediately alert teams when behavior changes negatively affect users and service-level agreements (SLAs). Making it easier to raise awareness of and remediate the issue before it can impact the end user.

3. Let AIOps drive your decisions

Pushing deployment info and context to AIOps creates even more awareness around delivery activities, providing a new source of data for DevOps teams to draw from to better inform future decision making.

AIOps solutions, which can generate data within their own dashboards, can better provide teams with choices and context in comparing test run and baseline results — drawing from multiple tests and deployments to identify regressions occurring during or between tests. Pushing this information to AIOps, in turn, further accelerates the software delivery pipeline and facilitates quick remediation for the delivery process.

4. Generate automated, operational resiliency

Resiliency and adaptiveness to change are key indicators of production quality today. AIOps solutions can ensure continuous resiliency, availability, and system health by automating manual operational tasks.

What's more, integrating AIOps with delivery automation sends configuration and deployment context directly to the solution, further enabling AIOps to better pinpoint root causes of abnormal behavioral changes; alert teams if or when a load test in production starts to affect overall system health; alert app teams if new service iterations are causing high failure rates; and provide detailed root-cause analysis on impact.

Today, greater operational resiliency means fewer issues, more consistent and reliable performance, and more robust digital experiences — all wins for DevOps teams and their customers.

As today's IT environments become increasingly dynamic, containerized, multi-cloud and multi-cluster, it's more essential for DevOps teams to capitalize on the power and productivity afforded by AIOps: driving business results, customer experiences and critical business outcomes effectively and at scale. Make sure your teams are equipped with the right AIOps toolkits is the first step in optimizing your AIOps journey. From there, leveraging those toolkits effectively is the best way to ensure that you're getting the most ROI out of your AIOps.

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4 Ways of Getting the Most Out of AIOps

Andreas Grabner
Learn more about Dynatrace

As organizations strive to advance digital acceleration efforts, outpace competitors, and better service customers, the path to better, more secure software lies in AIOps.

Coined by Gartner in 2016, AIOps — or AI for IT operations — has become an IT best practice only in the past few years. In short, AIOps offers developers and their DevOps and SRE teams a fast and automated solution for delivering observability (and precise insights) into their production environments at scale — making it easier for those teams to troubleshoot problems, identify root causes and remediate issues before they can impact the end-user experience or hinder the business bottom line.

Over the course of the next year, organizations expect production deployments to grow by 10x the current deployment rates, but as those deployments skyrocket, output volumes and production processes will also grow in complexity. An AIOps solution should be able to scale to meet that volume and process accordingly, but the fact is, not all can. Scalability and operational efficiency are only as effective as the AIOps solution you're leveraging.

As DevOps teams continue to adopt progressive delivery models — like Canary, Blue/Green and Feature Flags for upgrading and replacing individual services — and the volume of production deployments and configuration changes sees even more growth, here are a few of the things that your DevOps teams should keep in mind, as they look to make the most of their IT toolkits via AIOps:

1. Create test-driven operations

Bolster your AIOps' resiliency by testing auto-remediation scripts before entering production, rather than reactively.

For example, SREs can orchestrate a pre-production environment that's monitored by the AIOps solution. By loading tests and injecting chaos into this "test-driven operations" environment, and using it to validate auto-remediation scripts, your AIOps solution's capability for deploying auto-remediation code when an issue (inevitably) arises is further validated. Instead of SREs scripting and deploying code reactively (once an issue has been experienced), AIOps can deploy it proactively — fixing the issue immediately, thanks to having been "battle tested" for those scenarios in advance.

2. Push deployment/configuration data to AIOps

Linking events to a monitored entity makes it easier for AIOps to analyze and correlate behavior — necessary for going beyond simple correlations, to provide instead, more precise root cause answers. Pushing contextual deployment information (i.e., deployment, load test, load balance, configuration changes, service restart, etc.) to AIOps makes it possible to immediately alert teams when behavior changes negatively affect users and service-level agreements (SLAs). Making it easier to raise awareness of and remediate the issue before it can impact the end user.

3. Let AIOps drive your decisions

Pushing deployment info and context to AIOps creates even more awareness around delivery activities, providing a new source of data for DevOps teams to draw from to better inform future decision making.

AIOps solutions, which can generate data within their own dashboards, can better provide teams with choices and context in comparing test run and baseline results — drawing from multiple tests and deployments to identify regressions occurring during or between tests. Pushing this information to AIOps, in turn, further accelerates the software delivery pipeline and facilitates quick remediation for the delivery process.

4. Generate automated, operational resiliency

Resiliency and adaptiveness to change are key indicators of production quality today. AIOps solutions can ensure continuous resiliency, availability, and system health by automating manual operational tasks.

What's more, integrating AIOps with delivery automation sends configuration and deployment context directly to the solution, further enabling AIOps to better pinpoint root causes of abnormal behavioral changes; alert teams if or when a load test in production starts to affect overall system health; alert app teams if new service iterations are causing high failure rates; and provide detailed root-cause analysis on impact.

Today, greater operational resiliency means fewer issues, more consistent and reliable performance, and more robust digital experiences — all wins for DevOps teams and their customers.

As today's IT environments become increasingly dynamic, containerized, multi-cloud and multi-cluster, it's more essential for DevOps teams to capitalize on the power and productivity afforded by AIOps: driving business results, customer experiences and critical business outcomes effectively and at scale. Make sure your teams are equipped with the right AIOps toolkits is the first step in optimizing your AIOps journey. From there, leveraging those toolkits effectively is the best way to ensure that you're getting the most ROI out of your AIOps.

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Not All Networks Are Built for the Edge

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...

Why Observability Is the Missing Piece in Your Business Growth

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Gartner: Regular AI System Assessments Triple the Likelihood of High GenAI Value

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Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

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