DevOps performance is critical for a business to succeed, and automation can give a boost to this aspect. Learn how AI and ML improve the performance of DevOps.
Join the DZone community and get the full member experience. AI/ML and DevOps are hot buzzwords in the tech industry right now. Moreover, more than 15 million new jobs will be created in AI-related industries. Altogether, many companies are investing heavily in these fields to bring faster, more accurate, and more efficient tech solutions to their existing problems. Thus, demand for Artificial intelligence, machine learning, and DevOps is picking up an increased pace every hour. That’s the reason the application of AI/ML in DevOps is the major center of attraction among businesses. Image source Actually, the DevOps team can harness AI and ML to speed up development cycles and fix issues more quickly. Altogether, this combination of technology can also help in gathering advanced data about their customers to better understand their buying habits to cater to CX better. Apparently, the implementation of AI/ML tactics in DevOps is critical for both small and large organizations. However, a lot of businesses ask how AI and ML can be used in DevOps to acquire operational and cost benefits for the organizations. Surely, you, too, would be pondering about the application of AI/ML within DevOps. Let’s explore. Many businesses are doing their best to keep up with the ever-changing demands of customers, but it can be difficult for them. They might not have adequate time or resources because they’re trying new things all of the time. This is where DevOps combined with AI/ML comes in to picture. Moreover, the idea behind using AI and ML together in DevOps might seem like something that would benefit only large companies. However, you need to think again: 27% of small and large companies surveyed by ServiceNow said their company had hired someone „skilled“ at machine learning. Therefore, there’s hope yet if you need some help managing data input/output rates. The benefits of adopting AI and ML into DevOps environments are clear. The same survey found that 85% of C-level executives believe these technologies can offer substantial value in terms of accuracy, rapid decision making. It all would lead companies to improve profitability and efficiency. Image source However, the major aspect is how much easier the implementation of AI/ML in DevOps can be, especially when you have problems tracking an organization’s data. The reason behind this issue can be that the complexity involved has made things difficult before now. However, the implementation of AI and ML is critical in DevOps for organizational efficiency. Evidently, the DevOps team has always needed a way to stay organized; but with ever-changing applications and environments, they found themselves struggling more than ever before. This is where AI comes into play. It can help them track everything from development progress all the way up until delivery. It also eases down hassles on behalf of managers or technicians who must make decisions about what needs doing when there’s not enough information available at once! It’s no surprise that DevOps experts are eager to capitalize on AI and ML.
