DevOps is a set of practices that help accelerate software release management. With DevOps, companies deliver software faster - without any compromise in quality.
In the DevOps model, repeatable and error-prone tasks are automated to boost efficiency. With machine learning, DevOps practices can be enhanced even further to get more insight into the state of any system and make troubleshooting much simpler. Although DevOps practices have drastically changed how companies release their software and manage their infrastructure, there are still some challenges companies face. One of these challenges is the enormous amount of data generated by IT systems. DevOps teams don't have time to look at all that data, so they only look for exceptions. Additionally, the IT staff should be focused on architecture and management instead of trivial tasks.
Let's dive in and find how machine learning is changing the world of DevOps for the better.
As mentioned above, IT systems generate enormous amounts of data these days. DevOps teams cannot analyze all of the data in detail, so they focus on exceptions and set thresholds. This results in them ignoring most of the data and concentrating on deviations - this way, a lot of helpful information gets thrown out.
Machine learning helps analyze the totality of collected data and deliver detailed conclusions. Additionally, this provides DevOps teams with information on trends that may be significant in the long term.
Machine learning tools perform code reviews and analyses from the first stages of software development. This allows for early detection of code defects, security and other problems. Machine learning tools suggest code improvements based on the analyzed code and drive automated unit testing, helping developers save precious time.
With machine learning, the development of new applications is optimized in real-time. Machine learning algorithms examine past applications, their success in terms of build/compile, operational performance and testing completion.
Based on the collected information, tools offer recommendations for the project the developer is currently working on - helping developers write better, more efficient apps.
It is simple to focus on one variable, but it is also often necessary to look at the interactions of multiple measures to discover relevant trends. Traditional analytics are unfortunately useless in these cases. Thankfully, machine learning tools are able to discover correlations where other methods fail.
For example, if you use various tools to monitor the application's performance and health, machine learning tools help you analyze all of the data to provide a complete view of the app's status.
Every DevOps team has an alerting system that helps identify flaws quickly. While this is necessary, it can create chaos when several alerts with the same severity level come simultaneously.
With machine learning applications, teams can set up rules and machines can help manage system overload situations.
With an adaptive machine learning system, the system optimizes specific attributes based on your data input. The goal is to train the algorithm to maximize/minimize a value; the system changes the parameters during production to get you the best possible result.
Airlines use similar methods to optimize ticket prices to fill the planes and maximize revenue. Systems change the price of the tickets a few times a day to try and fill planes.
As we've already mentioned, if the DevOps team is personally analyzing only the data they need, they will miss out on potentially valuable and interesting information. They might even be curious about whether certain numbers correlate with each other.
Machine learning tools look at any subset or combination of data a team might want or need.
Machine learning enhances DevOps tools and helps companies focus on the tasks that bring business value instead of spending time manually analyzing and reviewing code, logs and processes. When we talk about DevOps, machine learning tools are here to stay and will play an essential role in the future of DevOps practices.
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