Tips for Putting AIOps Into Practice: What You Can Do Right Now

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Tips for Putting AIOps Into Practice: What You Can Do Right Now

Check out some tips for putting AIOps into practice and explore what you can do right now. Also, look at practical AIOps planning.

· AI Zone ·
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You know why AIOps is useful in theory. But do you understand how to put AIOps into practice and reap real-world benefits?

If not, this article is for you. Although the field of AIOps remains relatively young and AIOps-enabled tools are still developing, you can start taking steps now to organize your IT team and processes in ways that will allow you to take advantage of AIOps.

AIOps in Theory

At the theoretical level, the benefits of AIOps are easy to identify. AIOps lets IT Ops and DevOps teams use data, Machine Learning, and Artificial Intelligence to automate tasks that would otherwise depend upon manual human intervention.

As a simple example, an AIOps tool could identify a virtual server that is running out of disk space and then increase the virtual disk allocation automatically without requiring a human IT engineer to recognize the problem and intervene.

Similarly, an AIOps tool could trace a problem back to its root cause — an important function in today’s complex, software-defined, fast-changing architectures, where surface-level issues can be difficult to interpret without delving into reams of data.

Practical AIOps Planning

How do you operationalize these and other theoretical use cases for AIOps? The answer, as noted above, has not yet been fully fleshed out because AIOps is still a developing field.

Nonetheless, there are things you can do today to get ready to prepare for the possibilities of AIOps as AIOps-enabled tools enter into production.

Identify Processes that Can Best Leverage AIOps

An obvious first step in preparing to take advantage of AIOps is to identify the tools and processes that AIOps can help you improve.

AIOps won’t be able to help with every IT-related task at every organization. As an example, it probably won’t do much to help you plan long-term IT staffing needs (although it might help you plan day-to-day on-call schedules). In addition, you might have some tasks that you perform infrequently, or that are low-stake, and therefore don’t stand to gain much from AIOps.

At the same time, there are plenty of other processes, from monitoring to incident response to security analysis, where AIOps can come in handy.

Your first task in preparing to leverage AIOps, then, is to identify which processes you currently struggle with the most — then plan to use AIOps to improve them.

Identify AIOps-Friendly Tools

In some cases, AIOps may be integrated into tools you currently use that don't yet support AIOps. In other cases, you'll have to go out and find new tools to make the best use of AIOps.

It's therefore worth doing some research now to determine which of your vendors are already working to integrate AIOps into their tools, and which AIOps-friendly alternatives are available in the event that a tool you use today doesn’t look like it will take advantage of AIOps in the near future.

Provide AIOps Education

Education is always an important part of making the shift to a new technology, and AIOps is no exception.

AIOps education should begin with communicating to your team just how much your organization stands to gain by leveraging AIOps. (It might not hurt to mention how AIOps can benefit engineers personally, too, by doing things like reducing the number of alerts they have to handle during off-call hours and helping to automate tedious processes.)

Once you’ve achieved buy-in for AIOps within your team, and a basic understanding of the concept, you can take education further by training engineers in specific AIOps use cases, like monitoring and incident response

Streamline Data Collection for AIOps

You can’t do AIOps without data. In order to prepare for an AIOps-centric IT operation, you want to make sure that you are streamlining your data collection processes.

That entails identifying which data sources are pertinent for your AIOps use cases (and which ones aren’t—collecting unhelpful data could create a distraction), along with making sure that the data is properly prepared and stored.

The meaning of “properly” in this context will vary depending on which types of data you are collecting and which AIOps tools you use, but at a basic level, you should be thinking about things like how to ensure data quality, and perform any necessary data transformations in order to feed the best data you can into your AIOps tools.

Identify Your End Goals

The immediate effect of AIOps is to help IT teams do their jobs better, faster, and with less manual effort.

The long-term effect, however, is up to you and depends on how you choose to take advantage of the time- and risk-savings that AIOps provides. Will you use AIOps to scale up your infrastructure without having to hire more engineers? Will you refocus your DevOps team’s time on work like coding (which is much harder to automate) so that you can build software faster? Will you increase the amount of effort that you devote to manual code review and testing?

However you choose to use AIOps, it’s important to set a goal ahead of time so that you can ensure that you work constantly toward it and turn AIOps into real, long-term value. 

ai, aiops, aiops education, aiops planning, aiops tools, data collection, machine learning

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