DevOps, the combination of development and operations, is an innovative strategy in the software engineering domain and is being quickly adopted by industries. Every domain that has development and operations in their services has transformed the business operations, efficiency, and customer service with DevOps. With this technology, software no longer simply supports a business; rather it integrates into becoming the most integral part of a business in the long run.
A DevOps implementation software development supports enterprises by seamlessly assisting developers and ensuring enterprise efficiency. It helps the operations teams build, test, deploy and monitor applications with speed, quality and increased control. Efficient DevOps implementations frequently depend on a connected set of solutions, or a toolchain, to eliminate manual steps, decrease errors, improve team agility and scale beyond small, isolated teams.
How DevOps Support Agile in Business Efficiency?
DevOps extend agile in terms of the principles, as DevOps can provide a pragmatic extension for the current agile activities. For example, as DevOps stresses more on the communication and collaboration between developers and operators rather than tools and processes, it can achieve agile goals to reduce manual efforts and can create agile principles to the complete software delivery pipeline.
Agile supports DevOps: The Agile methodologies can enhance DevOps by promoting fusion between enterprises, their members, automation tools required in a project build, provide more employment. If the project is undergoing a test then it can provide the test measurement, metrics of cost, value, processes, knowledge sharing, as well as tools.
DevOps versus Agile: Agile methods for continuous integration and deployment have shared properties with DevOps, while DevOps itself cannot meet all principles proposed in the agile manifesto. Scaling DevOps to software-deﬁned carrier networks is, in a sense, like scaling agile development to large projects in multinational software companies.
Here’s How Artificial Intelligence (AI) Improves DevOps Automation?
DevOps implements a business-centric strategy and combines device Analytics with Artificial Intelligence. Therefore, while leveraging AI-efficient software solutions, DevOps teams can streamline testing, programming, deploying, and tracking their products. The implementation of AI in the life cycle not only provides utmost accuracy and performance but also decreases human intervention and time required.
This strategy is also essential in various automated processes and can seamlessly identify and fix software issues making it much easier for the DevOps team work more efficiently.
Advanced technologies such as Artificial Intelligence and Machine Learning solve multiple problems and strengthen DevOps’ operational complexities to transform industries quickly.
Improved Data access
Due to the huge amount of data that is generated on a daily basis in DevOps, the enterprise teams face issues while accessing and managing these data. AI helps to collect relevant data from multiple locations\sources and is also capable of organizing that data precisely. This data managed with the assistance of AI can be seamlessly accessed by resources and will help in fast and easy analysis.
Similar to data, DevOps team receives multiple alerts or notifications in huge numbers as well, but these alerts don’t have any sort of priority tags. Therefore to manually prioritize each of these alerts will be challenging and tiresome work for the team to handle. Here AI helps them to prioritize alerts. With the help of Artificial Intelligence, the DevOps team will not have to manually prioritize alerts but can have AI do it for them. Artificial Intelligence is capable of prioritizing alerts or notifications based on the previous behavior, algorithm, cause of the alert, and severity of the alert.
Superior Implementation Efficiency
In DevOps, humans have to mostly manage the rule-based environment, in other words, it is done in a completely manual process. With the implementation of AI, there can be a seamless transition to self-governed tasks and in turn increase efficiency. This implementation will help AI machines to work by themselves, with very less minimal human intervention. Hence making human resources free, so that they will be free to concentrate more on creative and innovative tasks.
Threats such as the Distributed Denial of Service (DDoS) are more active nowadays than before due to the entire work from home situation. These threats are capable of targeting any large or medium organization as well as websites. Artificial Intelligence (AI) and machine learning (ML) can assist in analysing and solving these threats. The enterprises may adopt an effective algorithm to distinguish between natural and unnatural conditions and then take necessary action accordingly. DevSecOps can be increased using Artificial Intelligence to enhance security, as it has a central logging architecture for identifying irregularities and malware.
Artificial Intelligence (AI) helps the QA team in process development and software test development. DevOps utilizes multiple testing standards, such as user acceptance testing, regression testing, and functional testing. A huge volume of data is generated from these test automation services. Artificial Intelligence recognizes the pattern of accumulated data and then identifies coding practices that led to the error. Hence with the help of DevOps, QA and test team can utilize this information to increase quality and test efficiency.
The future of enterprise development is going to concentrate more on intelligent automation that leverages data to understand various functions on its own. In the post-covid era, AI, DevOps and RPA are going to be a few of the unprecedented technologies that enterprises will heavily invest in. This is because several businesses are significantly enhancing their operations with just the basic digital transformation strategy and the advanced levels such as AI are sure to boost efficiency more.
Improving the complete development cycles in parallel to making certain that the most eminent quality code gets produced, is a significant hurdle mostly all DevOps teams encounter. With the help of Artificial Intelligence (AI), enterprises can accelerate every phase of DevOps development cycles by predicting what developers need even before they ask for it. Auto suggesting code segments, increasing software quality assurance techniques with automated test solutions, and streamlining requirements management are core areas where Artificial Intelligence is delivering value to DevOps today.
Ricky Philip is an industry expert and a professional writer working at ThinkPalm Technologies. He works with a focus on understanding the implications of new technologies such as artificial intelligence, big data, SDN/NFV, cloud analytics, and Internet of Things (IoT) services. He is also a contributor to several prominent online publishing platforms such as DZone, HubSpot and Hackernoon.