Over the last decade or so since the term ‘DevOps’ entered our collective lexicon – technology teams across the world have rushed to adopt this methodology, and for good reason. DevOps essentially breaks down the traditional barrier that we have seen between development and IT operations teams – where the former was charged with the ‘build’ aspect and the latter responsible for the ‘deploy’ aspect.
There are multiple benefits for enterprises adopting a DevOps lead approach to how they build software. The first and most important one is that this methodology accelerates the time-to-market for software releases through increased deployment frequency. Second, bug fixes can be released quickly and with minimal hassle due to automated tool chains. Third, the automation that DevOps brings dramatically improves the availability of developers to focus on innovation, rather than being caught in the mundane cycle of bug fixes and routine fire-fighting.
Now for the flip side; despite almost universal acclaim for this methodology globally and across industries, the adoption has been slow and patchy. Technology teams continue to reaffirm their belief in DevOps, but due to a multitude of concerns – ranging from skill paucity and a variegated toolset. The holy grail of DevOps – which is a 100% automation of the process from code changes to deployment on production servers – remains elusive. Further, a patchy adoption of DevOps has led to the creation of a two-paced overall technology environment – where we see parts of the software landscape of enterprises that have adopted DevOps with some degree of success while others are still mulling over the ‘how’ of tightly integrating it in the overall process.
Artificial Intelligence and Machine Learning techniques could be the panacea to these issues. AI will be a game changer in reducing the operational complexities endemic in DevOps due to the highly niche and distributed nature of the toolsets – which only encompass one or two of the five critical phases in the cycle – Planning, Creation, Verification, Releasing and Monitoring. Let's dwell on the same:
Let us start with the most obvious place where the intrinsic value proposition of DevOps and AI collides – automation. For improving the automation quotient in the DevOps process, AI can add significant value by reducing the need for human involvement across processes. Take for example QA and testing. We are seeing a massive barrage of testing platforms that can speed up the QA process across unit testing, regression testing, functional testing and user acceptance testing. These processes all typically generate a wealth of data – which can improve the accuracy of these tests as well as surface insights around persistently poor coding practices and errors. The latter is immensely helpful in identifying areas of development for coders and improving their performance.
Better Data Correlation across Platforms
In a wider technology ecosystem, teams use a plethora of development and deployment environments. Each team and their environment runs into its own set of issues and errors which are captured by monitoring tools. In the absence of a cohesive structure for communication, there tends to be little mutual learning across this teams, meaning that a lot of them go through siloed learning cycles. By bringing all of the issue data into a single data lake and applying AI, we can improve the correlation of data from multiple platforms, thus accelerating the learning cycle. Let us take the example of monitoring tools – ML can be applied vigorously here to absorb and uncover insights from data streams of multiple monitoring tools. This will help give technology teams an accurate and wholesome picture of application health.
Faster Redressal of Issues
While software bugs and issues are bad for enterprise performance in general, they can be devastating in situations where the digital platform is a customer facing property. Earlier, enterprises could afford to have issues logged into incident management systems for days and hours – which is not the case anymore. In our technology-first world, we need the ability to uncover and remediate performance issues much faster. Here again, a combination of AI and DevOps can be a game-changer. AI can help in prioritizing the most critical issues plaguing the application, collect all the relevant diagnostic information pertaining to the issue and even recommend a prescriptive solution. Further by observing the impact of the action taken after the issue was discovered through training data sets, the prescriptive AI systems can be even more accurate with its recommendations and help with issue remediation faster.
Better Security through Anomaly Detection
An important and topical application of DevOps is DevSecOps – which includes information and data security as a fundamental aspect of software development, across the lifecycle. DDoS (Distributed Denial of Service) attacks are increasingly prevalent across businesses and there is the constantly looming threat of hackers intercepting a secure system. DevSecOps can be augmented through AI to deliver maximal performance. By maintaining a centralized logging architecture to record suspicious activity and threats and running ML-based anomaly detection techniques, developers can accurately pinpoint potential threats to their system and secure it for the future. This proactive strategy can help mitigate the impact of DDoS and hacker attacks through a combination of DevOps and AI.
Increase Cross-Team Collaboration
This last point pertains to the incumbent cultural differences seen across developer and operations teams. The key sticking point between the two teams, culturally, tends to be developers’ inclination to release code fast and regularly and operations teams’ proclivity towards ensuring minimal disruption to existing systems. A DevOps culture brings dual accountability – reducing the time for deployment of releases – for both groups. While this fine balance can be hard to maintain initially, AI can have a transformative impact on improving collaboration between DevOps teams. AI-powered systems can help teams have a single, unified view into system issues across the complex tool-chain of DevOps and at the same time improve the collective knowledge of anomalies detected and the pathways for redressal.
We need Artificial Intelligence to accelerate the performance of DevOps. In an era where digital platforms are often the first point of interaction between consumers and brands, there is a great need for enabling faster development and deployment cycles, in a way that ensures a robust customer experience on these properties. DevOps is that framework – allowing teams to code, test, release and monitor software in a cohesive manner. With an infusion of AI, DevOps teams can improve automation, enable better collaboration as well as uncover and remediate key issues – thus helping this crucial framework achieve mainstream adoption and unlock great value.
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