Three ways DevOps will be more finely tuned in 2019

"DevOps is no longer a fringe movement."

That's the word from Wesley Pullen, chief DevOps strategist at Electric Cloud, in a presentation at the recent DevOps Enterprise Summit. There are now enough proof points on implementations to justify DevOps to senior executives as a strategy for attaining competitive advantage, he explains. "This is serious. This is a major competitive advantage for companies who start to adopt this -- not only at a team level, but also corporate wide." 

Pullen sees three trends shaping DevOps' breakout year of 2019:

Cloud and containers: "We've got to get to a point where we make life easier for your teams that are working to get delivery of software out into production or to the end-user." This paves the way to greater self-service as well. "You should be able to have things like a self-service catalog, where I can just click a button and say make all this happen for me," says Pullen. "We shouldn't have to always model it out  script it out and do all the work. There has to be capabilities like self-service, where my IT team says, 'here's our best practices give this capability. Now when I want to build my Kubernetes pipeline, I want to do stuff. So I just click the button and say, 'go get it for me, Go grab all the data for me and build my pipeline build my model for me, so my team can operate a little faster."

DevSecOps: Security needs to be the top priority of all efforts. "DevSecOps is not an acronym -- it is real," Pullen says. "It's something that we need to take seriously. We've got to stop using vulnerable frameworks, patches and things that we know that we need to get a little bit better on. We've got to start adding security into our pipeline. We need to start shifting left and allowing the security teams to have some stake in checking and validating the code that we are about to push into production."

Artificial intelligence and machine learning: Pullen says the emerging "DevOps operating system" will integrate AI and machine learning to move predictive analytics to the next level -- delivering actual business value. "It's how do we apply artificial intelligence and machine learning not as just buzzterms, but where would it actually apply in a real-life pipeline?"

AI and machine learning ultimately will drive efficiency through all phases of DevOps, Pullen predicts. "I want to be able to see if I were to make a couple of changes if my developers did this or if  my QA engineers did this could I get this release out a month early? could I be better could I get it out faster? Could I reduce the cycle time in my pipeline?  Because at the end of the day a pipeline is just the time I begin with an idea to the time I have it in production. Let's say it takes three months to get it done. What if my competition is doing it in two? Do you have the analytics to say, 'okay here's what we would need to do to get it out in two months'?"