What is artificial intelligence and how can it help your DevOps practices today? – TechRadar

By combining the roles of software development and IT operations, DevOps (opens in new tab) often encompasses so many tools and skills that too many of us get stuck working in a complex and time-consuming environment. Time that could be spent solving problems gets wasted on mundane tasks. By using artificial intelligence (AI), DevOps can automate complicated tasks that are easy for computers but hard (or boring) for humans. AI can also help streamline processes across the software development lifecycle (SDLC), allowing DevOps to focus on the work itself. In this guide, we cover how AI can be used throughout the DevOps cycle to improve productivity and security.

AI is the theory and method of creating computer systems that can automatically perform tasks normally requiring human, or greater, levels of intelligence. Computers rely on complex algorithms to perform these tasks, sometimes through explicit rules being provided to them, but more commonly through what is known as machine learning (ML).

ML is a subset of AI, where the system uses statistical methods to learn without explicit directions from a human operator. This requires sets of training data to teach the system desired outcomes. From there, ML can infer things about new data.

You may be using AI in your DevOps practices alreadyfor example, tools that make automatic code suggestions when writing software. The range and scope of DevOps tools available are growing, and AI is predicted to be a big part of automation in the coming year (opens in new tab).

Using automation to manage processes is a vital part of the DevOps approach. However, without AI, automation only executes actions based on explicit instructions provided by a person. AI uses a broader ruleset and a capacity to learn to improve performance over time. This allows AIand most commonly, MLto automatically perform complex tasks and eliminate the need for human intervention. These tasks include:

In each of these cases, AI is used to perform actions automatically that can reduce the workload of DevOps. This is great in theory, but how does it shape up in practice?

The most mature AI uses in DevOps are in applications that help programmers write code more effectively, those which manage monitoring and alerting, and those concerned with cybersecurity.

GitHub Copilot (opens in new tab) and Amazon CodeWhisperer (opens in new tab) are ML-powered tools that make relevant code suggestions to speed up programming. Both GitHub and Amazon integrate additional tools for testing within their environments.

Not every notification is important, and PagerDuty (opens in new tab) is an incident response platform that uses ML to minimize interruptions by improving the signal-to-noise ratio of important events to routine ones. As a basic example, instead of receiving alerts each time a service has successfully shut down and restarted, you might only receive an email if the service fails to restart. PagerDuty claims to provide up to 98% alert reduction.

Both Fortinet (opens in new tab) and Perimeter 81 (opens in new tab) provide high-performance network security tools that leverage AI. Fortinet provides resources for DevOps professionals, including GitHub repositories (opens in new tab) of tools and scripts to make setup and management of the software easier.

For those managing larger numbers of microservices or containers, especially in a multi-cloud or hybrid-cloud environment, Dynatrace (opens in new tab) uses AI to map, simplify, and manage the DevOps processes and delivery pipeline.

If youre interested in adding AI to your DevOps workflow, you probably already recognize many of the best DevOps tools (opens in new tab) available. Many of the tools that you already use are either adopting AI, or some, such as Selenium (opens in new tab), have additional software (opens in new tab) or plugins (opens in new tab) that allow AI to be integrated into them. Keeping on top of developments with the software you already use, and searching for tools that integrate with them, is a great way to get started with AI.

The DevOps culture has become an integral part of software development precisely because it allows projects to scale easily: spinning up 1,000 web servers is now as simple as creating one. Artificial intelligence takes the process one step further, allowing ever more complicated tasks to be left under the control of computer systems that learn and improve.

By using tools such as GitHub Copilot, Fortinet, and PagerDuty, DevOps professionals can harness the power of AI to produce a more efficient and secure SDLC. While there are many myths around DevOps (opens in new tab) trends, there is no doubt AI will continue to transform DevOps practices over the next couple of years.

TechRadar created this content as part of a paid partnership with PagerDuty. The contents of this article are entirely independent and solely reflect the editorial opinion of TechRadar.

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What is artificial intelligence and how can it help your DevOps practices today? - TechRadar