There's been a lot of media attention in recent years about how artificial intelligence (AI) and machine learning (ML) are going to change the world--how they're going to create new and interesting applications in fields as diverse as education, law, health care and transportation. But if I had to bet on a use case where AI and ML will create a tangible, lasting impact, I'm putting my chips on DevOps. DevOps is all about automation of tasks. Its focus is on automating and monitoring every step of the software delivery process, ensuring that work gets done quickly and frequently. While it doesn't eliminate human tasks--far from it--it does encourage enterprises to set up repeatable processes that promote efficiency and reduce variability.
The story of Artificial Intelligence (AI) and Machine Learning (ML) is all about hope and hype. On the one hand, there's a technology that promises to revolutionize fields as diverse as agriculture, manufacturing, education, and healthcare. On the other, there's so much media attention that it gets impossible to cut through the hype and, proverbially speaking, separate the wheat from the chaff. And though making heads or tails of it all is difficult, DevOps is for sure poised to capitalize on the opportunities that AI and ML offer, such as automation of tasks, data analysis, and improvement of efficiency. DevOps generates tons of data.
One of the most amazing dynamics within the DevOps enterprise community is seeing business leaders co-presenting success stories with their technology leadership counterparts. For example, Ken Kennedy (executive vice president and president for Technology and Product at CSG) and Kimberly Johnson (chief operating officer at Fannie Mae) described the achievements of their technology leadership counterparts and why it was important to them. I expect this trend to continue, especially given how COVID-19 has accelerated the rate of digital disruption. I believe this bodes well for all of technology. With the rise of hybrid (remote/in-office) product teams, upskilling and online training initiatives will expand.
As technology is progressing, artificial intelligence (AI) and machine learning (ML) are evolving in new sectors. Within software development, AI and ML drive programmers and testers to be more efficient as well as reaching their goals faster. With AI and ML, testers and developers have access to many capabilities that they didn't have in the past. Therefore, they are able to deliver better and more sophisticated software programs. Over the past few years, AI and ML have taken an important place within software development and we can wonder how much they bring to it.
Imagine if your software development team could use one simple testing tool having artificial intelligence to shorten delivery cycles, improve customer experience, update new features regularly and ramp up DevOps with best practices. What if we could share some insights on the growing trend of artificial intelligence in software testing. And how we used an AI plugin with a testing tool for one of our customers to automate functionality in less than a day. All this can be done having a basic knowledge of a programming language and testing of UI and API both on the web and mobile. You would be pumped to witness how artificial intelligence can revolutionize your software testing.