These two fields, which are now quite mature and slowly becoming unavoidable, are independent in their goals – DevOps is a business-based approach to software delivery, and AI is the methodology and technology that can be integrated into the system to increase functionality – but they can still be connected. With the help of AI, DevOps teams can code, test, deploy, and monitor their systems more efficiently. AI can also improve automation, quickly identify and resolve issues, and help teams collaborate.
DevOps, as a technological development (and shift) of recent years, primarily provides methodological solutions for the more efficient and transparent operation of frequently and rapidly changing complex systems (such as cloud systems and services) (and for the coordination of IT operators, developers, service management, and project teams required for this). DevOps, however, is not a standard or framework, but rather the conscious and appropriate assembly of several different building blocks – ITIL, Lean, Agile – into a single unit. It supports automation and continuous delivery, and encourages a culture of collaboration and learning to promote IT with better business value.
AI, in very simple terms, is the effort to automate intellectual tasks that are usually performed by humans. (So, technologically going beyond routine calculations, applying deep levels of analysis.) Artificial intelligence (and its approaches, Machine Learning and Deep Learning) can be applied to many business elements, as it can indicate problems and relationships that cannot be handled manually, mainly due to the huge amount of data to be processed.
With this in mind, AI can play a crucial role in increasing the efficiency of DevOps-style operations. It can increase performance, speed up development/deployment/operation cycles, and thus provide a more engaging user experience for services. AI systems can process DevOps operation metrics along with integrated architecture measurements, and by combining them, factors that reduce efficiency can be identified.
It is simply impossible to list everything in which AI can help DevOps, but a few key areas can be mentioned.
Extensive data processing across silos
DevOps teams often suffer from a lack of information. They do not have enough and/or inaccurate data, which can clearly hinder development activities. AI can collect data from multiple sources (even if it is not uniform), then perform consistent analyses after transformation and processing.
Automation
The most obvious area is a real “quick win”, through which spectacular results can be achieved in a short time. During the entire development/implementation and then operation cycle, many activities are routine. An AI model can automate these repetitive tasks, significantly speeding up the process. In addition, the need for human intervention can be minimized, so DevOps teams can focus on more complex, interactive problems, thereby increasing the use of human resources to a much higher level.
Testing
Thorough and careful testing – unit, functional, regression, security, usability, disaster recovery, etc. – can generate a large amount of data. With the help of AI, patterns can be extracted from this data set, which can identify, for example, inefficient coding practices or even unreliable test systems or test data.
Root cause analysis
The investigation of cause-and-effect relationships often does not penetrate deeply enough, the analysis is only superficial, therefore, after the elimination of the consequence (incident management), the real cause remains undiscovered due to lack of time and resources. AI - similarly to testing - can create analyses from the available data set, which can be of vital help.
Information security
While speed is an important aspect of DevOps operations, the protection of data and services is equally important. AI can also be applied in the area called DevSecOps. Security systems continuously log various threats, violations, and incidents, based on which an AI model can indicate anomalies, or thanks to its proactive prediction, DDoS or hacker attacks can be avoided.
Prediction
During the execution of DevOps cycles, virtually anything can cause a delay. Be it a device, a service element, a field or even a supplier delay. Given enough data, AI models can predict when this phenomenon is likely to occur again. This is not a simple prediction, AI’s pattern recognition ability goes beyond what humans can do.
Feedback
A key element of DevOps is continuous feedback. Data is collected at every step of every cycle, from log files, performance data, process metrics, system deployment times – practically anything. Applying AI models to this data can help identify problems in advance, and the avoidance of these problems can be incorporated back into the development cycles as application modification suggestions.
Effective collaboration
In the areas listed above, using AI – in addition to making DevOps cycles more efficient – promotes what is most important in the whole story: closer collaboration between development and operations teams. AI helps in unified management of systems, understanding complexity and, above all, sharing knowledge.