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The Role of ML and AI in DevOps Transformation

Gursimran Singh | 20 March 2023

The Role of ML and AI in DevOps

Introduction to AI and ML in DevOps

As we have seen Artificial Intelligence (AI) & Machine Learning (ML) have changed the conventional workflow of almost everything we come into contact with. The same goes for DevOps. AI & ML is changing the fundamental way that we think of DevOps. Most notable, the change is towards security as it recognizes the need to have comprehensive security that is intelligent by design (DevSecOps). Many of us consider this the next crucial step in the path after shortening the software development life cycle to ensure the secure delivery of integrated systems via Continuous Integration & Continuous Delivery (CI/CD)

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 mimicking human actions 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 on 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 increases precision and accelerates 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.

Revolutionizing DevOps with ML & AI

Today organizations are focusing on being data-driven to incorporate capabilities of AI & ML to achieve their ambition. AI & ML is experiencing humongous growth in multiple folds in almost all fields and is expected to expand aggressively.

With the inclusion of AI & ML, the organization has witnessed the world being digitally transformed. The coupling of ML & AI with DevOps will lead to a huge shift in its evolution. Firstly it sets DevOps as a crucial pillar for the organization's digital transformation ambition. For companies running on living data, the involvement of AI & ML with DevOps is to prove its wider value than ever before in every aspect, from efficient workflow to hardening of security for application development.

DevOps assembly lines help us to automate and scale end-to-end workflows of application across all teams and tools, which enable continuous delivery. DevOps Assembly Lines and CI Pipelines

What are the impacts of AI & ML on DevOps?

In a data-driven environment, scanning through the huge volume of data at a higher velocity daily to find critical issues can be done with AI with ease & efficiency leading to a reduction in the time & human-intensive workload.

With AI & ML involved, the manual configuration & automation of security aspects are focused on reducing the chances of faults & administration misconfiguration. Improvisation is done to reduce downtime & potential breaches by exploring vulnerabilities by an attacker. With AI & ML computing, analyzing & decision-making get data-backed & efficient.

There are several benefits of AI & ML on DevOps as mentioned below:

  • Efficient Application Progress: The application of AI with tools like Git will give you visibility to address irregularity in code volume, longer build time, improper resource handling & process slowdown & many more.
  • Quality Checking: ML makes effective Quality checking by building comprehensive test patterns based on learning from every release, thus leading to enhancements in the quality application delivery.
  • DevSecOps: ML integration offers DevOps secure application delivery by identifying behavior patterns for avoiding anomalies in key areas like system provisioning, automation routine, test execution & deployment activity, among others too. It also ensures that avoiding the inclusion of unauthorized code & stealing of intellectual property in the process chain are among the most common bad patterns.
  • Efficient Production Cycle: ML can be beneficial when analyzing resource utilization, and other patterns to find memory leaks leading to better management of production issues as ML is apt for the understanding of the application.
  • Emergency Addressing: ML plays a key role as it can analyze machine intelligence. It plays a crucial role in dealing with sudden alerts by training the system continuously to identify anomalies, thus helping to filter the process of sudden alerts to make it more effective.
  • Early Detection: The Ops team gets enabled to detect issues early by ensuring immediate mitigation response to allow business continuity with the help of AI & ML. Also, key patterns like configuration benchmarking are created to meet performance levels to predict user behavior to keep a continuous check on the factor that may impact customer engagement.
  • Business Assessment: ML also has a key role in ensuring business continuity for an organization, not just supporting the process development. While DevOps pays high regard to understanding code release for achieving business goals, ML tools deal with its pattern-based functionality by analyzing user metrics and alerting the concerned business teams and coders in case of any issue.
As testing progressively moving towards greater automation, we may be turning over most of it to Artificial Intelligence (AI). Benefits and Need of AI in Software Testing

What are the Challenges of implementing AI 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 Solutions to the 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 in DevOps 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 onward 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 having a good structure of communication. It means a lot for 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 that humans cannot perceive. This identification helps to handle the issue before its impact on the Software Development Life Cycle (SDLC).Read about Generative AI Solutions

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 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.
The companies that support remote working are growing, and this new working approach had an impact on DevOps culture. Latest DevOps Trends and Best Practices to Watch for in 2021

What are the Important points for implementation of AI & Ml in DevOps?

After knowing all the benefits of the implementation of AI & ML in the current workflow, we will now look forward to the points to consider during the implementation of AI & Ml with DevOps mentioned ahead:

  • Adoption of advanced API: The development team needs hands-on experience working with canned APIs like AWS, GCP, or Azure that enable robust AI/ML capabilities without creating self-developed models. Doing this development becomes easier as the development team can now work on further enhancements to the models as per the use case. Further add-ons of advanced patterns are integrated, and the related models are identified.
  • Implementing parallel Pipeline: As ML & AI are in an experimental stage, it’s crucial to consider the running of parallel pipelines to ensure that things don’t break in case of halts & failure. Implementing AI/ML is a stepwise approach to inlining the project's progress to avoid delay.
  • Use a pre-trained Model: With a well-documented model, the threshold of adopting AI/ML capabilities will drastically cut down the possession. Also, it will help in the recognition of user behavior or inputs based on past search patterns.
  • Training with Pubic Data: For the initial training of models, using public data sets can help you adopt AI/ML. It might not meet the exact requirement but it at least fills the gap to enhance the project visibility.


AI & ML can bridge the gap between humans & a huge volume of big high-velocity data to get insights. So with AI & ML, we can have a system that can analyze user behavior in every sense, be it searching, monitoring, troubleshooting, or interacting with data, and getting more competent & efficient by learning with past experiences.

Adoption of AI & ML with DevOps will lead to faster and more efficient SDLC. Also, it will lead to a secure automated process. It is a progressive step that organizations need to consider to catch up with the fast digital transformation. If an organization continues to do things conventionally & expects to get the same results, the new world being expected won’t happen. DevOps powered by AI & ML is the future that will become a reality soon.