Since 2009, DevOps has been touted as an improved way of handling development. Rather than separating development and IT operations — leaving the creation and deployment of applications to their respective departments — DevOps combines the two disciplines to achieve faster, more streamlined and continuous improvements and developments.
The reality, however, is that a decade out sees many organizations struggling to achieve the goals and benefits outlined in DevOps philosophy, one of these being the automation of as much of the process as possible. Here to the rescue are the two technologies that are poised to help solve these problems: artificial intelligence (AI) and machine learning (ML).
One of the challenges to successfully automating DevOps is the ongoing and continuous monitoring of deployed software. For better or for worse, the rise of Big Data means that many organizations simply don’t have the resources to keep up with the sea of data they have access to. Especially in large organizations — where thousands or even hundreds of thousands of users are interacting with the software — monitoring usage data can strain a team’s resources to the breaking point in the best of scenarios, and completely break it more often than not.
AI, however, excels at processing large quantities of data, sorting its relevance, identifying issues that need to be addressed and providing humans with digestible insights. This makes it infinitely easier for a DevOps team to quickly identify and address issues, rather than get mired down in searching for them.
In addition, AI can also be trained to automate much of the DevOps process, freeing up time and resources, while simultaneously speeding up development and deployment.
AI can also significantly improve DevOps security. As security threats continue to arise, cyber attacks often happen so fast that humans can’t respond quickly enough to head off the worst of the damage. AI, on the other hand, can rapidly identify a potential threat or an emerging attack and respond much faster than a human.
While AI is thrown around to describe everything from chatbots to virtual assistants, true AI has the ability to learn, grow, adapt and improve on its own. This is where ML comes into play. ML involves using algorithms to give an AI that ability to evolve, so to speak.
No more programming specific, complex if-then-else statements to try to account for every possible situation an AI may encounter. No more worrying that the decision-making programming will generate conflicts as the if-then-else statements become more complex. ML relies on data to continually train and improve the AI’s ability.
This means that the more an AI is used, the more data it has access to, and the better it will function in its capacity. ML can even help an AI learn from its mistakes by analyzing its responses, why a given response didn’t work and improving those responses in future operations.
DevOps continues to hold promise for faster, improved development and deployment for many companies. To succeed, however, companies will need to start implementing AI and ML to keep up with the ever-increasing flow of data being generated, as well as to protect themselves from cyber threats.
It is imperative for businesses to recognize that the bedrock of successful AI/ML integration begins with efficient data organization. No matter how groundbreaking these technologies are, overlooking the basics can impede progress and hinder the potential for innovation.
It is imperative for businesses to recognize that the bedrock of successful AI/ML integration begins with efficient data organization. No matter how groundbreaking these technologies are, overlooking the basics can impede progress and hinder the potential for innovation.
Meet Dmitry, our JS Developer. He tells us about his career at Opinov8, how he works remotely and how running helps him manage his time and productivity.
Meet Dmitry, our JS Developer. He tells us about his career at Opinov8, how he works remotely and how running helps him manage his time and productivity.
