Software developers (programmers) are key employees in many different companies. While they of course play an essential role in businesses that develop software as a product, they are also commonly hired to create in-house tools and applications for medium to large companies. As with any other area of technology, the way that things are done within software development has changed significantly. Today, those working in DevOps need to use tools and strategies that weren’t available not long ago. Two of the most important resources for developers today are actually machine learning and artificial intelligence (AI).
While machine learning and AI have been around in various forms for quite a while, it is only in recent years that they have really been used for common day to day tasks. This is largely because these technologies are only now becoming affordable enough for the average business to take advantage of them. This can be done either by standing up internal systems to power AI (still typically reserved for large corporations), or more commonly, harnessing machine learning or AI through a cloud platform. For example, companies that use Amazon Web Services (AWS) as part of their cloud infrastructure can access the AWS AI systems as needed. With these tools available, those in DevOps need to understand how they can use machine learning and AI to take their jobs to the next level.
Automating Common Tasks
Developers spend a large portion of their day performing repetitive and mundane tasks that are associated with the main goal of developing software. In addition to being tedious tasks, they are often places where human error can cause problems. For example, AI tools can analyze code for many different types of errors and automatically correct them. As any developer knows, it is not at all uncommon for something as small as forgetting to close a bracket to cause significant problems. AI tools not only identify these types of common errors but can accurately fix them without developer intervention.
Helping to Create More Efficient Code
Another area where AI and machine learning can help with the DevOps process is by helping to identify (and in many cases, fix) inefficient code. When working on complex projects that go through years of updates and patching, it is not at all uncommon for even the best developers to use inefficient code. While this typically won’t cause a program to fail, it can increase run times and increase the number of lines of code quite significantly. Using machine learning, the system can actively analyze code for inefficiencies, such as coding in a process multiple times rather than making a call for a subroutine. Depending on the developer’s preference, this can either be automatically fixed (in some cases) or the developer can be alerted so they can determine the best course of action.
More Robust Testing for Cleaner Releases
Testing code can be an extremely time consuming and difficult process. It is not enough to simply run a new program to confirm it works. To the extent possible, developers need to go through and perform every conceivable task that an end user would perform to see if it works. In addition, this should be done in multiple different environments to make sure there aren’t any conflicts or other problems that shouldn’t exist.
Rather than doing this manually, or worse, publishing software for users to access as a form of testing, an artificial intelligence system can do it for you. AI can run millions of simulations across thousands of simulated environments in the amount of time it would take an individual to run just a few. This type of deep testing will dramatically increase the end user experience with new software, software patches, or updates to existing programs.
Discover End User Needs
Understanding the needs of the end user is one of the most important parts of DevOps. While asking users what features they need or what problems they run into is a good idea, it is not always fruitful. End users typically don’t really know what is possible for developers, so they either don’t know what features to ask for or they ask for something entirely outside the scope of a given system. Using machine learning, it is possible to gather massive amounts of data on the activities that end users are doing with a program. This will allow developers to incorporate features tailored to their exact needs.
Machine learning and Artificial Intelligence are revolutionizing many aspects of technology. Those who work in DevOps need to make sure that they are taking full advantage of this rapidly advancing technology. The more that it is used, the more effective it will be at helping developers push out the best software possible.