Today, irrespective of the size of the enterprise, be it tech mammoths like Google, IBM, and Facebook or smaller companies and startups, or the type of industry including healthcare, transportation, and finance, enterprises are increasingly favoring the adoption of AI and machine learning technologies to optimize their performances and functionalities. DevOps is no exception. AI and ML are perfect fits for a DevOps culture which is all about the automation of tasks. Its focus is on automating and monitoring every step of the software delivery process to make sure that the work gets done quickly. The two technologies can process vast amounts of information and learn patterns, anticipate problems, and suggest solutions to help perform menial tasks, promote efficiency, and reduce variability.

DevOps’s primary tenet is the use of continuous feedback loops at every stage of the process by using monitoring tools to provide feedback on the operational performance of running applications. Machine learning is enhancing the continuous feedback loops that are critical to DevOps by helping the advanced monitoring platforms to identify problems and make recommendations proactively. AI and ML also help organizations to get rid of one of the biggest challenges while moving to a DevOps methodology. Communications channels become more streamlined and proactive by using automation technology, chatbots, and other systems initiated by AI. These two technologies not only help organizations in correlating data across platforms and tools but also in managing a flurry of alerts. On the whole, AI and ML smooth out the process for DevOps to reach its goal of unifying development and operations.

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