Artificial intelligence has taken the tech world to a new level of automation. Today, almost every specialization demands the intervention of machine learning to develop AI technologies that help businesses do more with less time and resources. Still, some organizations question whether leading with AI is a good investment.
For DevOps, the answer is a resounding yes. AI can enhance DevOps practices to accelerate the pace of software releases, helping businesses achieve continuous delivery. This enables programmers to release software about 10 times faster and allows programs to be reviewed before they are released.
AI also has improved DevOps culture, from enabling more efficient decision-making processes to code quality enhancements and automated operations. So, let’s take a deeper dive into how AI impacts DevOps.
Monitoring and managing all the data generated in DevOps environments involves a high degree of complexity, making it difficult for teams to collect and use data effectively. In addition, the volume of data any given team may receive can reach well into exabytes, so AI tools provide much-needed assistance. After all, analyzing massive data sets manually would take too much time for a human and won’t meet the demands of modern businesses.
What’s more, surveys show that 57% of developers in the United States have less than five years of experience. For this reason, software testing must be an ongoing and in-depth process to check and double-check that no vulnerabilities impact the security of the code. AI can supplement manual review to increase speed and accuracy in finding bugs.
That being said, integrating AI into DevOps functions doesn’t mean developers aren’t necessary, even though their roles have evolved considerably. Backend developers are involved in the development and operations sides as they take on ops tasks and tests, especially around cloud-based infrastructure and cybersecurity. And frontend developers will always be needed for the technical support of creative design—which no one can do better than humans.
In today’s dynamic and fast-paced world, developers need to release code faster than they used to while the operations teams ensure that the existing systems function with minimal disruption. This partnership is easier when integrating AI, as it makes the collaboration between development and operations teams more efficient. AI-powered systems provide a unified view into systems and glitches in the complex chain of DevOps.
Short answer: No. AI is supplementing human activity in DevOps to make things more efficient and secure.
Due to the complexity of DevOps requirements, people have been looking for a more automated solution that could assist humans in performance and accelerate the processes. What’s more, you can expect to pay around $60 an hour for an experienced backend developer in the U.S. This means using AI to automate the grunt work can be a big cost saver as well.
Here are a few ways AI transforms DevOps functions:
Distributed denial of service (DDoS) and related attacks have become a widespread threat to the security of websites and online services these days. AI-powered tools have the ability to identify and mitigate these threats. In addition, AI can help in the pivot to DevSecOps in teams still using traditional DevOps.
AI security tools detect anomalies and threats based on real-time data and by analyzing past behavior. In this way, AI has played a vital role in protecting many organizations, including schools and colleges, from cyberattacks by supplementing human security experts.
The massive amount of data that needs to be collected, organized and analyzed in DevOps is beyond human capabilities. However, AI has resolved this issue by compiling data from multiple sources and organizing it for analysis. This drastically improves and accelerates the process of DevOps.
Software development and software testing has accelerated with the integration of AI. As a result, various testing types used by DevOps, such as user acceptance testing, regression testing, and functional testing, are more efficient and accurate than ever.
These tests produce a large amount of data that would take humans ages to identify and collect manually. With the help of AI, identifying patterns and coding practices that lead to errors and vulnerabilities has become convenient and fast. This data can then be used later by the DevOps team to increase their efficiency and improve development practices.
DevOps teams sometimes struggle to react and respond to the alerts during incident response. With no priority tags attached, it becomes challenging for the team to prioritize incidents effectively. A more efficient, comprehensive alert system should be capable of spotting flaws instantly and marking the more severe ones as higher priority. That way, the team can tackle the issues more systematically and resolve them without failure.
AI can assist in prioritizing the responses to alerts based on certain factors, including the intensity of the alert, the source of the alerts and past behavior. This leads to efficient management in situations where systems get flooded with massive amounts of data and have to respond immediately. AI-powered alerts are particularly important in DevOps security, so this is a critical benefit of AI in the discipline.
DevOps teams are often burdened by the managerial tasks of a rule-based environment which decreases their time spent in more innovative and creative areas. When AI takes over, these tasks become self-governed, increasing efficiency and reducing human intervention. AI systems can work on their own, sparing humans from these tasks to focus more on innovation and creativity.
This way, AI has turned the DevOps into self-governed systems that no longer require the rule-based, human management of analysis. This should resolve the complexity of research that DevOps teams were struggling to achieve and enable faster adaptation.
The primary function of DevOps is to use monitoring tools to gather feedback from every stage of operations and then use that to improve software development and delivery processes. Performance monitoring tools use machine learning to gather information such as performance matrices, log files, datasheets, etc. The feedback collected is then used to track potential issues in advance and make suggestions to resolve them accordingly. The application is then altered by applying these suggestions to make it more efficient.
Artificial intelligence is driving increased efficiency in DevOps functions. However, it would be a stretch to say that it is taking over, as there are still many areas that require human intervention. The idea behind AI integration is not to change the players but to change the game.
Even though AI is assisting humans in accelerating software development, developers still lead many aspects of the craft. But it also seems impossible to perform DevOps functions at the required pace without the integration of AI. So, if you wish to release software at a pace that keeps up with modern tech trends, you need to incorporate AI in your DevOps functions.
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