The robots are coming to DevOps… But think less Ex Machina and more machine learning (ML).
Everyone in the tech world will have some idea of what DevOps is, and it’s present in most industries such as Banking, Healthcare and even Gaming. Teams spanning countless sectors across the globe have adopted it, and the results speak for themselves — particularly in a CI/CD-focused market that requires faster dev cycles and smaller release windows, due to growing consumer demands.
Although DevOps already uses a range of different tools, issues like security, data handling and resource management remain challenging for teams. In fact, one of the biggest challenges for DevOps is automation. And, as well as its variety of other functions, automation is an area in which AI is perfectly suited to lend a hand.
How can DevOps take advantage of AI?
Due to the changing consumer landscape, technology organizations everywhere are under huge amounts of pressure to innovate at the quickest rate, in order to remain competitive. With several opportunities to modernize already presenting themselves, many companies have identified AI in DevOps as one of the key areas in which they can significantly improve their pace.
As customer, partner and application data increases exponentially, teams require faster onboarding and deployment, and managing testing becomes critical – AI is already helping DevOps teams gain some serious momentum.
AI in DevOps is all about speeding things up and making everything easier – the dream for almost anything in life – and doing so without already-sparse resources (like time and manpower) taking a hit. This is why AI in DevOps is heavily focused on automation and predictive mechanisms.
So, if you’re stumbling around the internet looking for an answer to the question, “How can a DevOps team take advantage of artificial intelligence?” You’ve come to the right place.
Here are eight ways you might want to implement AI in DevOps.
8 ways AI in DevOps has its advantages
1. Enhanced software development
The main area in which DevOps truly gets the most bang for its buck from AI is throughout the software development process. While it’s true that solutions such as Internal Developer Platforms have proven useful in helping developers better manage the software development lifecycle, AI can touch on multiple aspects of the dev process by automating a lot of the manual, time-consuming tasks.
Everything from incremental testing to on-the-go code reviews to identify poor practices and common errors. AI can enhance the software development process by automating and streamlining. In the case of incremental testing, where users can detect errors by testing modules one by one, AI can automate the process for the developer. Similarly, for on-the-go code reviews, AI can take the job of spotting errors manually out of the hands of the developer, freeing up time to spend elsewhere.
2. Improved data organization
Organization is key when working with lots of moving parts, but sometimes it’s simply impossible for one developer to handle all alone. This is why one of the most common uses for AI in DevOps today is in the field of data management and analytics.
These days, DevOps and apps produce significant amounts of data (massive, really), and AI can quickly sort, manage, and analyze the data to produce better insights, find patterns, and identify problems quickly.
3. Better resource management
Especially in large teams, managing resources (anywhere from on-prem processing and data centers to cloud instances) becomes a challenging juggling act. AI assistants can find gaps, understand bottlenecks, and optimize resource usage to reduce stress and strain at the worst times.
It can also help DevOps by automating workflows, and this in turn may streamline other adoptions within an organization, like a change management process, for example. So let’s leave juggling where it belongs — at the circus.
4. Stronger forecast failure
It’s no secret that predictive problem-solving is simply more efficient than post-failure problem-solving. One of the benefits of better data analysis is pattern detection, and AI can help identify success and failure trends long before they become problematic.
As such, using AI for devops can reduce failure rates by helping pinpoint red flags long before they’re an issue. It also enables real-time alerts while prioritizing responses based on previous results, source, and depth.
5. Optimized testing at every level
Testing is a critical part of the CI/CD and DevOps methodology, but it’s also one of the most time-consuming. AI and automation can significantly reduce the manual labor of testing and allow developers to build, test, and fail faster, leading to better results in much less time.
Functional testing, regression testing, and user acceptance testing create a vast amount of data. AI-driven test automation tools help identify whatever may be responsible for recurring errors by reading the patterns in the data. This type of data can then be used to the organization’s advantage.
6. Swifter root cause analysis
Finding bugs is only one tiny part of the problem-solving process.
Teams often move so fast that they only care about finding a bug and fixing it, but not so much about understanding why it happened. But no one wants to keep going in and fixing the same bug over and over.
AI allows teams to dive into the root cause much faster and resolve the issue permanently, to avoid having to constantly solve it. To do this, AI makes use of the patterns between the cause and activity, to identify the root cause lurking behind a recurring failure.
7. Improved collaboration
Good news for the homebodies. Developers no longer need to be working in close proximity, since AI enables teams to work better together from anywhere — no matter where they are in the world or how far apart they might be.
AI can do this by providing insights into how specifications and shared criteria represent unique requirements, localization, and varying performance benchmarks. And, of course, this also benefits those who decide to collaborate in person.
8. Better anomaly detection
Unfortunately, nothing in the world of DevOps is ever as straightforward as we’d like. Complex application systems bring with them yet another problem area — error tracking and analysis. For instance, in an IoT environment with several microservices in use along with its numerous touchpoints, pinpointing failures with accuracy and speed simply isn’t possible.
There are troves of raw data to go through and this is often too time-consuming for devs to do themselves. However, AI models can easily handle vast amounts of data without nearly as much effort as it would take the human brain… even a developer’s.
So, what’s the verdict?
There are several opportunities for DevOps teams to gain a competitive edge, simply by adopting a few AI technologies that will enable them to move forward with confidence. And it’s not about taking dev jobs away from real-life developers, it’s all about making life easier by automating the lengthy, monotonous and time-consuming tasks that simply don’t need to be done by a human.
In short, the point of integrated AI solutions within DevOps really comes down to marrying the developer’s expertise with ML capabilities.
If you want to find out a bit more about AI in DevOps, feel free to check out our blog, which is packed full of useful techy information.