Machine Learning and Artificial Intelligence are both designed to help humankind break free from menial, overly simple tasks. While we don’t live in a futuristic utopia where robots are autonomously taking care of our every needs, both of these technologies can help in the here and now with DevOps.
By its very nature, DevOps is also focused on automating the routine and repeatable actions of a team and business. So, it only makes sense that AI and ML blend seamlessly with this methodology. New tools and technologies are making this realm of DevOps more efficient than ever before, as seen in this source: https://www.appoptics.com/monitor/java-performance.
Each of these technologies can benefit the world of DevOps in different ways. Not only do you need to know how they can help, but understanding the difference between the two can further highlight their capabilities.
The Big Difference
People throw around AI and ML as if they were interchangeable, but this simply isn’t the case. While they both fall into the Big Data discussion, saying both are the same would be like comparing a T-2 Terminator to Johnny 5 from Short Circuit.
The easiest way to separate the two is by a machine’s level of intelligence. AI focuses on machines that can carry out complicated tasks or protocols. That could be as simple as placing doors on automobiles in an assembly line or something more in-depth like C# exception handling. As AI advances, machines are created with the ability to handle tasks that make them smarter than the average computer.
AI isn’t anything new, though its modern applications are certainly groundbreaking. Even a calculator’s ability to complete complex mathematical computations is an example of artificial intelligence. Today’s work in AI focuses less on a machine’s ability to complete calculations and more on its ability to mimic human decision making like trading stocks or driving a car.
ML is an application of AI, but it focuses on letting machines learn for themselves. By giving a machine access to data, it can then gain its own understanding and act as it sees fit (within certain parameters). An excellent example of ML is Google’s Neural Machine Translation system and its ability to understand translations between languages without any prior knowledge.
Computers and machines designed for ML are created to think like humans, much like modern AI, but are then given access to additional data via the internet. Some create music based on user preferences, while others work to understand if a customer comment was a complaint or a compliment.
The DevOps Revolution
Now that you know the difference between AI and ML, you might already have an idea of how they incorporate into the DevOps methodology. The name of the game for any business is to meet customer expectation, which are constantly changing. It takes a lot of data analysis and computation to help meet those changing expectations, which where these two technologies come into play.
AI and Big Data go together like peanut butter and jelly. Not only can these machines collect data from multiple sources, but they can prepare their findings for evaluation as well. All of that data remains regulated since AI is its own self-regulating system.
AI is also, in part, self-governed. This removes the complexity of relying on individuals to improve efficacy in rules-based management systems. The benefit is automation, and AI can continue to automate several tasks within the DevOps system.
As for ML, open source projects hold vast potential for making DevOps more efficient. Model Asset eXchange and Fabric for Deep Learning might be costly at first, but the ROI on optimization isn’t something to be overlooked. The more responsive IT operations are, the better service a business can provide its customers.
Bridging the Gap
Back to satisfying those ever-changing customer expectations. Both AI and ML hold the power to make DevOps the ultimate machine, one where both humans and computers work together in a near flawless system. From fixing errors within an app to a quicker, more positive customer service experience, users’ expectations are readily met no matter what they are.
By freeing up smaller tasks through automation, DevOps employees can focus on larger tasks at hand or better refine tasks that previously didn’t receive the attention they deserve. Now, identifying a singular point within an exabyte can happen almost automatically. Of course, it takes more than one application of these technologies to make this happen.
Depending on your company’s flow, you will need to develop a DevOps stack that targets each part of the process. Deciding where to apply the skills of your team and where to apply AI or ML can be challenging, but the tools available can often help determine the best course of action.
Buying isn’t always the best option, though. In some cases, choosing to build custom layer is far more beneficial when you already have a strong DevOps infrastructure. Applying either technology from there allows team members to focus on creativity, innovation, and productivity.
Looking to the Future
As of now, only around 27% of CIOs have adopted AI and ML or hired a team member with skills in either. The process seems to be a slow one, but experts in the industry agree that DevOps stands to benefit the most from both advanced and basic technologies in either application.
The issue is actually implementing AI/ML into your existing framework. It’s a complex process that requires a lot of effort, making things difficult for your team to manage on top of their usual tasks. Training to understand the systems in place and the time that requires is also challenging.
Some companies have found success by implementing both AI and ML slowly. While numerous layers are required for optimum performance, this trickle of technology allows your DevOps team to adapt quickly with each new addition. Hiring even a single employee with machine learning knowledge, making them the point of contact between your team and new tech (so to speak), allows for quicker integration as well.
Regardless of how you apply AI and ML, the benefits and ROI are guaranteed. It might be a slow process, but the future of DevOps relies entirely on these two technologies.