Artificial intelligence (AI) and Machine Learning (ML) technologies are rapidly transforming business functions, including software development. In its search for efficiency, the industry has slowly shifted from the traditional Software Development Life Cycle (SDLC) to an agile development environment.
Over the last decade, DevOps has become an industry standard. Its main goal has been to improve product delivery/development by encouraging communication between software developers and IT operations. Across industries, the need for developers-operations balance has led to the rise of DevOps ethos. According to Grand View Research, the DevOps market size is expected to reach $12.85 billion by 2025 with an 18.60% CAGR.
For a culture focused on efficiency and automation of tasks, it’s no surprise that AI and ML find their application in DevOps. From enhancing continuous feedback loops to software testing, AI/ML and DevOps complement each other perfectly. As IT operations become more agile and dynamic, AI can iron out the kinks in the system.
Gathering Key Data Insights
A DevOps process generates significant amount of data across servers and logs. Combing through big data to find specific instances can be cumbersome and not cost-effective. In this data deluge, ML optimizes application environments stay afloat with real-time data analysis.
Using supervised learning and training data, developers would be able to identify errors that would otherwise be missed in large data clumps. Machine learning can also be employed to analyze insensible data to identify patterns and behaviors that can be used towards data analytics. As ML reduces noise-to-signal ratio data silos are broken down for teams to use across product development. Developers are no longer limited by self-defined thresholds, giving room to assess data trends.
Correlation across Monitoring Tools
As dev teams expand, multiple monitoring tools are used to assess data and also check application health and performance. The layered algorithms of AI/ML accept multiple data streams allowing correlation of data across multiple monitoring tools. ML systems connect disparate data systems to provide real-time health assessments of applications.
Optimizing DevOps process
Employing adaptive-ML, DevOps teams can optimize specific values or metrics towards a certain goal. Neural networks are trained to maximize a single value/ parameter; enabling the system to adapt and change during the production phase itself. This ensures the optimization of values throughout the development lifecycle.
Enhancing Security and IT operations
Mining of large complex datasets helps in gaining meaningful insights in predicting product and server failures, avoid technical drawbacks and facilitate decision making in DevOps business framework. Most security protocols are implemented the end stage of the development life cycle. In case of banking sectors, DevSecOps – the culture of integrating security within the DevOps process, is gaining ground.. This philosophy emphasizes on ‘security as code’, allowing streamlining testing parallelly to security and compliance reviews. The use of AI-based digital security technology allows banks to meet market demands while continuously monitoring potential security risks.
Smarter Resource Management
By automating routine and manual tasks, AI/ML systems aide in efficient resource management. Teams have more time to concentrate on efficient development and coding practices.
Software Testing and Shift-Left
Software testing is another aspect wherein AI/ML applications can be leveraged to evaluate coding errors in test results. Test automation is a critical part of shift-left testing. AI helps in running multiple diagnostic tests to identify the cause of failure. In the case of a test failure, AI is in a position to rectify the error even before the product hits the market. This approach involves minimum human intervention. Whereas, the continuous feedback loop aides developers write efficient code, reducing errors thrown by the system.
The capabilities of AI/ML-driven solutions in conjunction with DevOps is ever expanding. As organizations work towards identifying bottlenecks, hiring skilled talent is the impeding factor for the AI-driven organizations. On the foundation of a strong DevOps infrastructure, AI/ML will be synergic tools to increase efficiency. AI will continue to make inroads into more business cases with vertical-specific solutions that would transform business processes.