Artificial Intelligence makes machines intelligent. Machines are programmed, so they become able to think like humans. Artificial Intelligence led to mimic human action ranging from simple to complex tasks. It is taking technology to the next level. We will explore in this blog how Artificial Intelligence in Transforming DevOps.
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DevOps is a set of practices. It integrates the process of Software development and Information Technology. DevOps helps to build, test, and release software faster. The primary role of DevOps is to take continuous feedback of the process at every step. DevOps fill the gap between Development and Operation.
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DevOps generate a large amount of data. This data is used for monitoring, streamlining the work process, and other tasks. In some significant tasks, a massive volume of data is generated. So employees are not able to handle that large amount of data. In that condition AI technology is used for computing and decision making. Artificial Intelligence increase precision and accelerate production. AI enables all types of automation for business processes. Hence help to save time and increase efficiency. The future of DevOps depends on Artificial Intelligence.
Challenges in DevOps
A high degree of complexity is involved in managing and monitoring the DevOps environment. It becomes difficult for the DevOps team to deal with the magnitude of data in today’s dynamic and distributed application environment. The team has to deal with data that can be in Exabyte. Thus it becomes challenging for a human to handle massive data and make operations to solve customer’s issues. It takes too much human time for handling that data. A human can’t analyze the whole data manually.
How is AI transforming DevOps
Improved Data access
A lot of data is generated daily in DevOps. The team is facing issues while accessing that data. Artificial Intelligence helps to compile data from multiple sources and also to organize that data. This data will help in the analysis and give a good picture of trends.
Distributed Denial of Service (DDoS) is very active these days. It can target any big and small organization and website. Artificial Intelligence and machine learning can be used to identify and manage these threats. An algorithm can be used to differentiate normal and abnormal conditions and then take action accordingly.
DevSecOps can be increased using Artificial Intelligence to enhance security. It has centrally logging architecture for detecting anomaly and threats.
AI helps in enhancing process development and testing of development. DevOps is using various types of testing, such as regression testing, user acceptance testing, and functional testing. A large amount of data is produced from these testing. AI identifies the pattern of collected data and then identify coding practices that led to the error. Hence DevOps team can use this information from onwards to increase their efficiency.
DevOps Team receives several alerts in huge numbers. But these alerts don’t have priority tags. It is very difficult for the team to handle all alerts. Here AI helps them to prioritize alerts. AI can prioritize alerts using past behaviour, source of the alert, and intensity of the alert
Superior implementation efficiency
In DevOps rule-based environment is managed by humans. The transition of this to self-governed tasks increases efficiency. Using AI machines can work by itself or with minimal human intervention. Hence make human free so they will be available to focus more on creativity and innovation.
The primary function of DevOps is to collect feedback from every stage. For this purpose performance, monitoring tools are used. These tools used Machine Learning features such as performance matrix, datasheet, log files, and many more. According to this feedback, they make suggestions and apply them.
Benefits of integrating AI in Devops
Artificial Intelligence helps systems for intelligent decision making based on real-time data.
DevOps produce a large amount of data. For humans, it is very difficult to analyze data. Artificial Intelligence analytic technology helps to identify and solve problems. So it helps in problem identification and resolution. Hence it increases process efficiency and customer satisfaction.
Data correlation across platforms
In wider technology environment teams has a plethora of development and deployment environment. Each team and environment has its own set of problems and errors during monitoring tools. There is little mutual learning across teams due to not have a good structure of communication. It means a lot of them to go through the siloed learning cycle.
Using Artificial Intelligence, we can accelerate the learning cycle. It can improve data the correlation of data from multiple platforms by bringing all issues to a single data lake and applying Artificial Intelligence (AI).
Machine Learning helps in prediction errors based on data. AI can predict signs of failures because it has the ability to read the pattern. AI can see indicators of failures which human cannot perceive. This identification helps to handle the issue before its impact on the Software Development Life Cycle (SDLC).
Risks of AI in DevOps
- It is necessary to train the system with correct data. If data is not adequately trained, then it can give us the wrong results.
- Different users can have different software and hardware requirements. Models they used can also be different. It can be possible one is using Pytorch, and the other is Tensorflow. In that case, it is difficult to synchronize between them.
- As in today, Artificial Intelligence is less established, so it becomes difficult for a technical leader to convince their superiors to invest in AI-based tools. Investors are more likely to invest in those apps and projects that are more familiar and established.
As discussed in this blog, AI helps to increase efficiency. Artificial Intelligence helps the DevOps team to focus on tasks that need human intelligence and creativity. Thus reducing errors, increasing speed, and productivity. It helps in managing complex data. Artificial Intelligence enhances automation and accelerates the performance of DevOps. AI enables faster deployment and development life cycle. And in-turn improves customer experience. DevOps allow the team to code, test, release, and monitor software. AI improve all stages by improving automation.