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The Role of Artificial Intelligence in Transforming DevOps

Acknowledging Data Management
          Best Practices with DataOps


Introduction to AI in DevOps

Artificial Intelligence and DevOps are interdependent. DevOps is an approach to deliver software. Artificial Intelligence is the technology used to integrate the system for enhanced functionality. AI and ML are perfect for a DevOps culture, from the decision-making process to automated operations and code quality enhancements. The future of DevOps looks promising with AI and ML. We will explore in this blog how Artificial Intelligence is transforming DevOps.
40% of DevOps teams will be using application and infrastructure monitoring apps that have integrated artificial intelligence for IT operations platforms by 2023 Source - Gartner.

What is Artificial Intelligence?

Artificial Intelligence makes machines intelligent and 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. Read everything about Artificial Intelligence and its Applications

What is DevOps?

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. 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, but employees cannot 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.

What are the 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 solve customer issues. It takes too much human time to handle that data. A human can't analyze the whole data manually. Here is the complete guide to Challenges and Solution to Adoption of DevOps. 

How is AI transforming DevOps?

Advanced technologies like AI and ML resolve various issues and alleviate DevOps' operational complexities to transform industries rapidly. Listed below are the various aspects in which AI is transforming DevOps.

Improved Data access

A lot of data is generated daily in DevOps, and the team is facing issues while accessing that data, but 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 help in identifying and managing these threats. One may use an algorithm to differentiate normal and abnormal conditions and then take action accordingly. DevSecOps can be increased using Artificial Intelligence to enhance security. It has a centrally logging architecture for detecting anomalies and threats.

Software Testing

AI helps in enhancing process development and Software Testing of development. DevOps uses various testing types, 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 identifies 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 challenging for the team to handle all alerts. Here AI helps them to prioritize alerts. AI can prioritize alerts using past behavior, source of the alert, and intensity of the alert.

Superior implementation efficiency

In DevOps, a human manages a rule-based environment. The transition of this to self-governed tasks increases efficiency. Using AI machines can work by themselves or with minimal human intervention. Hence make humans free, so they will be available to focus more on creativity and innovation.

Feedback Loop

The primary function of DevOps is to collect feedback from every stage with the use of Monitoring tools. 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.
Artificial Intelligent is taking DevOps to a new level of Accuracy, Quality, and Reliability. Source- Ten Ways AI Is Accelerating DevOps - Forbes

Top Five Benefits of Integrating AI in Devops

AI is making the process of deploying, designing, and developing faster. Listed below are the other various aspects of benefits of Integrating Artificial Intelligence in Devops.

Decision making

Artificial Intelligence helps systems for intelligent decision making based on real-time data.


DevOps produce a large amount of data. For humans, it isn't easy 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 from multiple platforms by bringing all issues to a single data lake and applying Artificial Intelligence (AI).

Handling failure

Machine Learning helps predict errors based on data, and AI can predict signs of failure because it can 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).
Explore the Benefits and Need of Artificial Intelligence in Software Testing

What are the Challenges 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. The models they used can also be different. It can be possible one is using Pytorch, and the other is Tensorflow. In that case, it isn't easy to synchronize between them.
  • 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 improves all stages by improving automation.