When DevOps meets AI, the golden era of intelligent operation and maintenance is about to open?
In the next few years, DevOps (the intersection of software engineering, technical operations and quality assurance) and the IT operation and maintenance department will face new challenges, but this statement sounds a bit redundant because they are the most important. The responsibility is to solve the difficulties and overcome the challenges.
With the significant changes in processes, technologies, and tools, it has become increasingly difficult to deal with these issues. In addition, enterprise users have been putting pressure on DevOps and IT operations teams to require everything to be resolved by clicking on the application. However, in the background, dealing with these issues is completely different. Users can’t understand how difficult it is to find a problem, let alone solve it.
Artificial Intelligence and its impact on IT operation and maintenance and DevOps
More than a decade ago, artificial intelligence was just a concept of hype, but it has now been widely used by people from all walks of life for a variety of purposes. Combining big data, artificial intelligence, and vertical domain knowledge, technologists and scientists have been able to create amazing breakthroughs and opportunities that were previously only seen in science fiction and movies.
As IT operations become flexible, dynamic, and complex, the human brain is no longer able to keep up with the speed, volume, and diversity of big data streams, making artificial intelligence a powerful and important tool in optimizing analysis and decision making. . Artificial intelligence helps fill the gap between humans and big data, providing the necessary operational intelligence and speed to humans, greatly reducing the burden of human troubleshooting and real-time decision making.
What can AI do?
In all of the above cases, one thing is common. As discussed at the outset, these companies needed a solution that would help IT operations and the DevOps team quickly find the problem from the mountain of log data entries. To identify the log entry that adds to the trouble of your work environment and crashes the application, is it too simple if you simply know what type of error is in your log data? Of course it will also reduce some of the workload.
One solution is to build a platform that collects relevant data from the Internet, observes how people use similar devices to solve problems in their systems, and scans your system to identify potential problems. . One way to achieve this is to create a system that simulates how users investigate, monitor, and resolve events and allows it to underestimate how humans interact with data rather than analyzing the data itself. For example, this technology can be similar to Amazon’s product recommendation system and Google’s PageRank algorithm, but this is focused on log data.