How Can a DevOps Team Take Advantage of Artificial Intelligence?
DevOps teams are responsible for delivering software applications at an unprecedented pace while maintaining a high level of output and performance quality. With the rapid advancements in technology, DevOps teams are constantly looking for new ways to improve their processes and make their work more efficient. One technology that has gained significant attention in recent years is Artificial Intelligence (AI).
With the launch of GPT-4 announced by Microsoft on March 11th, 2023, our reality is once again about to be changed with the advent of a much more robust and versatile version of ChatGPT hitting the internet. Artificial intelligence is progressing at a rapid pace, and it has finally become a tool that is easily available and accessible to everyone.
Like most businesses and development teams, DevOps teams are also focusing their work around Artificial Intelligence since the latest AI tools are very good at automating mundane tasks, reducing human errors, and improving the overall quality of their software applications.
Let’s take a quick look at what DevOps teams are, then dive deeper into how they are increasingly integrating AI solutions in their work.
What are DevOps Teams?
Back in the days of yore (the mid-2000s), the software industry was starting to witness a boom in dedicated software applications. With everyday users and the industry as a whole requiring more and more specialized software, the workload of software houses started to increase almost exponentially.
Previously, Software Development and Operations used to be two separate functions or departments in an organizational setting. The developers were single-mindedly focused on writing code, and operations were in charge of deploying it and ensuring that it met the requirements of end-users. However, this meant that there was a huge communication gap which reduced the efficiency of both teams.
At that moment, a brilliant Belgian IT consultant by the name of Patrick Debois came up with the idea of forming a “DevOps” team in 2009. The new approach required that people from both departments would come together to work on a single project. This would increase the quality of the end product and also increase the pace at which the teams could develop the software features and capabilities.
Today, DevOps teams are an integral part of all major software companies in the world. Since there is an increasing demand to provide a higher quality product in less time, they are always on the lookout for resources that can help them increase their overall efficiency and speed. This is where AI comes in.
The Many Ways DevOps Teams Can Use AI
The term predictive analytics refers to the use of mathematical modeling based on historical data to predict future events and trends. Predictive analytics is sometimes used to predict future demand, but most DevOps teams prefer to use it to find potential issues they might have with the current approach they are using so that they can fix any problems before they invest too much time as a result of working in the wrong direction.
According to the data engineers at RTS Labs, there are various types of data predictive analytics that you might come across. Data analytics includes, but is not limited to, user interactions with the software, past incidents, and system data. User interactions give information on how the user interacts with the software. This can include the number of clicks, typing speed, and frequency of use.
For example, if a user is wasting too many clicks on a function, it might suggest that there is an issue that needs to be fixed. Similarly, past incidents like the frequency of certain types of errors when using certain parts of the software can pinpoint more issues. You might have experienced this yourself, as MS Windows programs often give different error codes based on how an application crashes.
Lastly, and perhaps most importantly, is system data. If you go to your ‘Task Manager’ on Windows and click on the ‘Processes Tab’, you can see CPU usage, memory usage, disk read/write rate, and network usage. DevOps teams use these metrics frequently to check whether the software they are developing is consuming way too many resources as it is scaled or not.
Optimizing Resource Management
In today’s day and age, system resource management is a dire issue. Remember the good old days when you could run Microsoft Excel on mere Kilobytes of RAM? The video game DOOM, which came out in 1996, required so few resources to run that it could run on a simple pregnancy test device! Today, if you use Google Chrome and open one tab too many times, it will eat up more than half of your RAM. This points to the mismanagement of system resources by software, and AI can help us finally solve this huge problem.
AI can keep an eye on a lot of things that are too costly or simply impossible for a human to catch. For example, your software might perform thousands of small functions at a time, and one of them might be causing sudden surges in CPU usage. AI can catch issues like these and allow you to easily fix them.
Similarly, AI can readjust resource allocation on-the-fly, ensuring that no part of the program takes up more resources than is strictly necessary. Similarly, if your software is cloud-based, it can juggle the workload across thousands of devices to fully optimize the use of your resources.
Optimizing scarce resources is very important for the future. Although we are developing faster and more efficient hardware at a rapid pace, the quality of our software is lagging, reducing our net gains in speed and technological improvement.
We probably do not need to explain to you what chatbots are. Chatbots have permeated all forms of online business activity at a rapid pace, and over the years, their effectiveness has increased at an extraordinary pace. Chatbots are used everywhere, and top business enterprises around the world, like Visa, MasterCard, and Barclays, have great chatbot features for customer support.
Customer service represents a major expense for any business. Before chatbots, companies had to invest large sums of money into maintaining a big customer services department. The best they could do otherwise was introduce lengthy FAQs that most customers may or may not be able to understand.
Furthermore, customers would not be willing to go through the trouble of looking for answers themselves and would want customer service representatives to fix their issues for them. With the advent of chatbots, most common issues could be solved easily by the chatbot, leaving development teams to focus on improving the software based on the data collected by the chatbot.
Chatbots can function 24/7, ensuring that customers have an easily accessible service and that companies do not have to spend excessively on retaining a full-fledged customer services staff.
Security is also an important concern with software applications today. Malicious attacks have increased over the years, and especially today, when almost everyone owns a laptop or a smartphone and is using many applications on these devices, it is easy for their security to be breached. AI can come in very handy when developing robust applications whose security cannot be easily manipulated.
DevOps teams are increasingly using Artificial Intelligence (AI) to improve their security position by automating the detection and response to potential threats. AI can analyze log data and detect incidents like unauthorized access to sensitive data or changes to applications. By using AI, teams can stay ahead of online threats and efficiently take advantage of the latest security technology.
Additionally, AI can automate vulnerability scanning in applications, thereby, identifying potential security issues quickly and saving time and money for the business. ML (machine learning) algorithms can enhance vulnerability scanning potential by automating detection, identifying unknown vulnerabilities, prioritizing them based on their likelihood and impact, and continuously monitoring for new vulnerabilities.
Moreover, ML algorithms can also reduce the number of false positives generated during the scan by analyzing multiple data points and accurately identifying real vulnerabilities.
AI is the Future of DevOps
We have touched on various facets of how AI is changing the DevOps landscape, but the true scope and potential of what AI can do for DevOps teams in the near future can make anyone’s head spin. From doing mundane things like automating repetitive tasks, testing, and code quality, to highly advanced operations like the integration of Natural Language Processing systems like ChatGpt in Bing, AI is transforming the world of DevOps at a very rapid pace.
Although all these technological innovations are revolutionary, RTS Lab’s experts are of the opinion that there is a learning curve associated with using AI in any industry, and it will take time for new DevOps teams to integrate the technology into their daily processes and streamline it within their existing architecture.
Other drawbacks of using AI in software development include the potential for bias in the algorithms used, the need for extensive training and education to implement AI effectively, and the possibility of AI replacing human jobs. There is also the risk of AI systems malfunctioning or being hacked, which could lead to serious consequences for the businesses using them.
That being said, there is no denying that AI is in the future for DevOps teams. AI may well be on its way to revolutionizing everything ranging from writing scripts for YouTube videos and writing books, to creating mesmerizing art and maybe even bringing actors from old times back to life using deep fakes.
We all know that the right software can inspire new ways of working in the workplace. It is consequently a vital corporate asset, and you should choose your software with care so that it meets your organization’s requirements.
At RTS Labs, we make software that gives you an unfair advantage.
Our elite cross-functional teams bring you the agility of a startup and the scalability of an industry leader.