One of the most frustrating things to experience in business: hurry up and wait. But when it comes to DevOps, that’s exactly what many companies have been experiencing. Though many jumped aboard hoping to speed up production and release cycles, in a majority of cases, they haven’t experienced the efficiencies they were hoping for. But I don’t think you should lose hope just yet. Turns out AI and DevOps may be the answer today’s frustrated companies have been waiting for.
AI and DevOps: Better Together?
First, let’s make sure we’re all on the same page when it comes to understanding DevOps. As I’ve shared before, DevOps started as a way of making companies more agile—of increasing the speed of building/testing/releasing software to the public without sacrificing quality. It makes sense, right? In a world where technology is moving at record pace, you don’t want your new products or product improvements to get hung up in QA. You want processes that move at the speed of light, just like your customers want them to.
Still, with so much data taking over the world right now, DevOps is getting a bit overwhelmed. Teams are wondering how they can sort through all the information they’re supposed to be sorting, vetting, and sharing with one another in order to get those enhancements made. That’s where AI and DevOps comes in. Just as it does in any other case, AI helps analyze and automate to keep processes—and improvements—rolling. In that sense, the partnership makes total sense.
AI and DevOps: How, Which, and When?
So—it makes sense to join AI and DevOps. But how do we do it? Which processes do we use it for? And when do we start? The short answer: start now.
As I’ve said before, it makes virtually no sense to launch a data or analytics protocol at your company if you don’t have the AI and machine learning to support it. Your team will be burnt out and ineffective almost instantly. The same holds true for AI and DevOps. While some say AI delivers “power” to analytics, I’d go so far as to say data has no purpose without it. So, if you’re not incorporating some form of AI into your DevOps program, I’d reiterate you need to start NOW.
But which processes should you partner with AI? And how should you pair them? In my view, any process that involves optimization, efficiency, or even security is ripe with opportunity when it comes to AI and DevOps. One analyst reckons AI is 1,000 times faster than humans in flagging anomalies in various processes and coding. Thus, basically any process that involves reviewing, comparing, and improving should be automated ASAP.
But what about making decisions? After all, it’s one thing to find a discrepancy in quality or security. It’s another to find a way to fix it. That’s where machine learning comes in. Where once humans needed to spend hours reviewing for issues, finding correct solutions, and implementing them to see if they work, AI can do those things automatically—even instantly—with better results. AI and DevOps, for instance, can use predictive modeling to not just offer a possible fix, but to let you know exactly what will happen with that fix so you can make a more informed choice. In essence, it saves man hours in every single step of the process of the DevOps process.
AI and DevOps: Who’s Leading the Way?
Honestly, AI and DevOps will look different for everyone. It’s way less important to worry about who is doing it well than it is to find how YOU can use it to improve your own processes right now. Having said that, I can say AI and DevOps is being used in all industries, from healthcare to remodeling, to bring improved efficiencies to life. This is not an “industry specific” solution! It’s one that can do the dirty work for anyone who needs it.
As I mentioned previously, mastering DevOps and truly reaping its benefits requires a shift in culture. By eliminating the sheer drudgery of sorting and processing data, I think AI and DevOps could help engender a greater willingness among employees to make that shift—leading to higher efficiencies altogether. Mark my words: AI and DevOps are tremendous partners for change.
The original version of this article was first published on Futurum.
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