Why and How You Should Integrate AI and Machine Learning into App Development and DevOps Efficiency

Featured article by Prasanna Singaraju, CTO and co-founder of Qentelli

AI is a technology that can be applied to every business process, as it runs on data. With the advent of internet and computing power, humans have created troves of data in past decades. The same goes for AI adoption in different business processes and DevOps is one such process where AI and algorithms hold a lot of potential. The rapid adoption of DevOps has created a similar amount of data within the companies to be utilized for creating intelligent AI systems accentuating their Continuous Integration/Continuous Delivery (CI/CD) pipelines.

How AI Fits into DevOps Ecosystem and Why You Should Adopt It

AI and DevOps can create new and agile business models with customer centricity. Both are being adopted by companies rapidly to deliver fast and accurate results. DevOps can be an enabler for AI as it creates continuous and automated processes for CI/CD to speed up software development lifecycle and AI creates intelligent systems based on past data and analysis to work on their own with minimal human help. AI holds a lot of potential in tasks that are manual, repeatable, prone to human errors and have enterprise data to train AI systems.

The companies that have adopted DevOps have already seen a remarkable difference in terms of brand value, customer satisfaction, lower wait times, quality applications etc. DevOps adoption by many global giants that are disrupting markets in terms of rolling out new features, updates, beta platforms, across multi-platforms with continuous and faster feedback loops in place.

Since innovation is all about reaching the market at the right time with no compromise on the quality, without embracing DevOps or automation, it is impossible for companies to keep pace with the changing customer dynamics and adapt to their demands within few days. AI with DevOps can create a competitive edge for the companies looking to adopt it and laggards will face a tough time in overtaking this competition, or probably they can never compete with the AI-first businesses.

How does AI Integrate with DevOps?

DevOps stresses moving from traditional IT systems to a continuous way of software delivery. DevOps is a combination of various processes and many of them are repetitive and needed to be done on a continuous basis to ensure faster feedback loop. Continuous integration and build tools allow developers to develop code and merge them with the source code repository with integration tools. Continuous testing tools help in providing feedback to developers require to fix the code errors, or bugs before being pushed into production because of robust and continuous testing tools.

The first level of use case that is already being implemented for integrating AI with DevOps is using predictive analytics for automating performance, security and functional testing. Companies are already having lots of data around logs, metrics and other data from their application environments. AI can be used to identify these patterns and predict outcome in the environment and code causing performance or production issues. The direct impact can be in the form of reducing downtime in the production environments and taking proactive actions to avoid downtimes and rollbacks. AI also helps in developing advanced alert and notification systems to improve DevOps test and feedback processes leading to the fast elimination of errors.

The second use-case of AI integration with DevOps can be seen in continuous testing, an integral part of CI/CD pipeline and is indispensable for building successful CI/CD pipeline. Continuous testing involves a lot of repetitive tasks with lots of data already being held by organizations, making it a perfect candidate for AI. Testers do a lot of repetitive tasks such as the creation of test environments, generating test codes, running same tests again and again on systems under test, these things can be easily outsourced to AI.

AI integration in DevOps is going to go beyond identifying patterns and predicting possible outcomes to eliminate errors in a production environment. Companies that have reached DevOps maturity already started analysing possible areas of combining AI in their CI/CD pipeline. One of the ways how AI can increase CD efficiency is by identifying errors in code and write codes on behalf developers to fix bugs based on actions being taken in the past when such errors have been encountered. Till the time AI algorithms do not mature, approvals for rewritten code must be done manually by developers.

One more area of CI/CD pipeline that will see a remarkable impact from AI is Ops part from DevOps. Many companies that claim to adopt DevOps are still configuring infrastructure manually or they don’t have defined a unique infrastructure that can cater to all the code changes in the repository. In future, AI will play a role in recommending or setting up infrastructure or making consistent intelligent infrastructure that is being adhered to security policies, compliance rules for complete transparency and seamless delivery of applications across multi-platform deployment.

Is AI in DevOps Just for Big Companies?

To answer in one word, No! AI and DevOps are industry and size agnostic and presents clear benefits to any company in terms of less time to market, continuous delivery and deployment, less downtime, reduced outages etc. Taking everything into consideration and how fast AI and DevOps use-case are coming up, AI integration with DevOps is the thing of the present and not future.

Companies that are progressing towards adopting AI in DevOps processes must aggregate the data of their entire CI/CD lifecycle in a centralized computing platform to develop AI algorithms to identify trends, predict outcomes, and write auto-generated codes to rectify them.

As of now, DevOps helps teams in identifying errors fast and resolve them faster, with AI in DevOps systems will be able to rectify errors on their own without any human interference and be proactive by better-predicting capability across all the environments, giving more rapid releases to fit the trend with upcoming technologies like Smart cities, building, IoT and many more.