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The Impact of Artificial Intelligence & Machine Learning in DevOps


If one had to bet on a use case where AI and ML are likely to create a tangible, lasting impact, then he’ll probably put his chips on DevOps. DevOps is all about automation of tasks. It focuses on automating and monitoring every step of the software delivery process and ensures that the work gets done quickly and frequently. While it doesn’t eliminate human tasks, it does encourage enterprises to set up repeatable processes that enable promoting efficiency and reducing variability.

AI and ML are considered to be perfect fits for a DevOps culture. They are able to process vast amounts of information and help to perform menial tasks, while freeing the IT staff to do more targeted and strategic work. They also learn patterns, anticipate problems and suggest solutions to issues.

Is it Worth Investing in Machine Learning and Artificial Intelligence for DevOps Efficiency?

It is very clear that the future of application development is about intelligent systems, utilizing data that is being created and letting the systems learn on their own. This is a new paradigm shift and organizations are going to take over. Some companies are already driving business and operational efficiency and it will only get better with time. The automated process of identifying patterns and suggesting corrective measures is likely to bring out the true power of DevOps. Businesses that are trying to drive efficient DevOps practices using AI/ML, will see more faster deliveries, less failure rates, improved customer satisfaction ratio and seamless experience on connected devices.

Many organizations are beginning and more than eager to adopt the model to gain efficiencies in application development and other areas of their business. Artificial Intelligence and Machine Learning is supposed to have an all-encompassing relationship with DevOps. DevOps’ goal is to unify development and AI/ ML possess the capability to provide an amazing access to computing and processing. They have provided DevOps with the ability to test a wide variety of algorithmic strategies, as well as provide storage and data-management capabilities to handle the processing and testing. 

These are some ways in which AI and ML can and will change DevOps for the better. 

  • DevOps, combined with the data requirements of AI, can increase the velocity of new applications.
  • AI brings three distinct capabilities — self-learning, prediction, and automation, that can improve current DevOps practices such as Continuous Integration (CI) and Continuous Deployment (CD).
  • AI and Machine Learning feed off data with self-learning capabilities, making AI and ML techniques extremely beneficial, if embedded into the DevOps tasks and processes.
  • When software code is being developed, AI/ML can keep track of the extent to which the end-user experience is being addressed, by simulating various possible scenarios.
  • AI and ML can help keep track of production performance and establish links to past issues.
  • With AI/ML embedded into the DevOps process, the DevOps teams can get an insight into how the code is performing.
  • AI helps to manage the growing volumes of data in DevOps environments.

AI/ ML – Influence on DevOps and Driving DevOps Evolution

AI/ML changes the way DevOps teams develop their tools, deliver their production goals, and deploy the changes within their functions. Developers are able to improve an application’s efficiency, and enhance business operations.

  • Improved Data Accessibility
  • Effective Use of Resources
  • Greater Ease of Implementation
  • Driving efficiency True DevOps Way
  • Leveraging the Power of Data in DevOps
  • ROI by Optimizing DevOps

The Future is Now

Organizations that want to automate the DevOps first need to establish a strong DevOps infrastructure. Once the foundation is created, then AI/ML is applied for increased efficiency. AI/ML assists the DevOps teams to focus on creativity and innovation by eliminating inefficiencies across the operational life cycle. It enables teams to manage the amount, speed and variability of data. This, in turn, results in automated enhancement and an increase in DevOps team’s efficiency. Interventions by AI/ML on DevOps will not only make code development, deployment and production runs much more predictable, but also provide a continuous innovation process.


Nairita Goswami, Marcom Specialist, RapidValue


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