According to a DevOps strategist, an increased fine-tuning of DevOps will take place in 2019 and the years ahead. This was stated during a recently held DevOps Enterprise Summit. Right now, there are sufficient points of evidence on DevOps implementations that justifies it as a viable approach to achieving competitive advantages.<\/p>\n
Here are the innovative ways that govern how DevOps will be fine-tuned:<\/strong><\/p>\n It\u2019s the right time to make things easier for developers aiming at software delivery to the end-user or into production. Also, this has the additional benefit of making room for increased self-service. Nothing stops users from having a sort of self-service catalog, making it possible to simply use a click of a button alongside voicing out what you want done. Always scripting out, modeling out, and just laboring from start to finish should be curtailed or gotten rid of.<\/p>\n There should be self-service capabilities. In essence, when trying to build a Kubernetes pipeline for instance, there should be a capability to achieve it by giving out commands such as \u201cgo get the entire data\u201d. So, there is a faster operation for the team.<\/p>\n Also, increased machine learning and artificial intelligence integration will happen this year. According to a DevOps strategist and experts, the DevOps operating system about to emerge is going to integrate artificial intelligence and machine learning. The aim is to transit predictive analytics to its next level — the resultant effect would be authentic business value delivery. Machine learning and artificial intelligence aren\u2019t just buzzwords, but the future is in designing innovative ways to apply them. Now, they will be defined where they will apply when it comes to a real-life pipeline.<\/p>\n Ultimately, machine learning and artificial intelligence will promote efficiency through the entire DevOps phases. So, some of the questions in this regard should be: could tasks be done faster? What are the methods that can help cut down the cycle time in a pipeline? A pipeline is from the time of idea commencement to when it is ready or available in production. Now, assuming the pipeline lasts two days, but the competitor is getting it done in just one day, there is a major loss of competitive advantage. So, there has to be a way of getting it done in one day just like the competitor, or even less than one day.<\/p>\n Prioritizing security when it comes to DevOps is a trend to pursue in 2019. This is why DevSecOps is not just a buzzword but a reality. It\u2019s time to quit adopting vulnerable frameworks, and to commence integration of security into the pipeline. The security team should re-enforce efforts in checking as well as validating code prior to pushing it into production.<\/p>\n#1: Cloud and Containers<\/strong><\/h2>\n
#2: Machine Learning and Artificial Intelligence<\/strong><\/h2>\n
DevOps Security<\/strong><\/h2>\n
Charter Global<\/h2>\n