The revolution of (AI) and DevOps

The combination of artificial intelligence (AI) and DevOps has the potential to revolutionize the developer experience and unlock their creativity.

AI has already demonstrated its ability to generate, test, and deploy software code, and tools like ChatGPT can write code and fix bugs. As the importance of developer experience rises, AI, low-code, and no-code technologies are expected to enhance the future of DevOps and developers’ roles. By utilizing AI to handle repetitive tasks, developers may be freed up to focus on more creative and innovative work. Lee Atchison, a software architect and cloud computing expert, notes that computers and machine learning excel at tasks that humans struggle with, such as spending extended periods looking at the same thing. Ultimately, the combination of AI and DevOps could lead to a more enriching and fulfilling experience for developers.

According to Hope Lynch, Senior Director of Platform and Technology Strategy for CloudBees, the convergence of AI, machine learning, low code, and no code is making this one of the most exciting times for DevOps. Developers are becoming more integrated with the business, and there is more focus on improving the developer experience. Lee Atchison, a software architect, believes that machine learning and AI are the future of DevOps and can greatly enhance productivity. With thousands of code releases happening daily, AI tools can help scan system reliability to prevent issues. Machine learning and AI are critical not only from a development standpoint but also from an operations perspective, especially as more and more applications are deployed through SaaS and cloud services.

As AI becomes more integrated with DevOps, what does it mean for software developers? According to Hope Lynch, developers are using AI as a coding partner to come up with additional ideas and find shortcuts, but not to replace them. AI is helping developers have access to a wider range of ideas faster, allowing them to focus on their creativity. Lee Atchison adds that machine learning is useful for analyzing large quantities of data to look for anomalies, which can then be viewed by humans to figure out what’s going on. This helps developers more efficiently continuously deploy applications. Parag Doshi believes that AI is another tool that will allow developers to devote more time to areas they were originally hired to do, such as being creative, as they become smarter and more efficient in building and testing applications.