Mar 2, 2021. By Aleena Mathew
The term DevOps is not a new buss word in current business and IT organizations. The practice of DevOps was present in the system for a very long time. The concept of DevOps is a much-needed implementation to any business. In an era where digital transformation is taking its place, there is a great need for faster development and deployment cycles. That's where the DevOps shined bright. DevOps is that cultural principles – allowing teams to code, test, release, and cohesively monitor software. One major benefit the DevOps concept possessed was, the time-to-market for software releases drastically increased through increased deployment frequency. One major challenge faced was the release of the software after bug fixes. But luckily, with the DevOps on hand, bug fixes can be released quickly with minimal trouble due to the automated toolchains. Apart from that, developers benefited the most from the automation that DevOps brought. They were able to focus more on bringing innovations into the system.
But with all these positives that the DevOps poses, there was always a flip side to all these positives. With the high volume and variety of data generated and dynamically changing applications, there was a great struggle for the DevOps team to identify issues and fix them. This lead to a setback for the deployment of applications, which further lead to chaos in the system.
AI will be a game-changer in reducing the operational complexities in DevOps.With the infusion of AI, DevOps teams can improve automation, enable better collaboration, and uncover and remediate key issues. As per Gartner, 40% of DevOps teams will be utilizing app and infrastructure checking applications that have integrated Artificial Intelligence for IT Operations (AIOps) platforms by 2023. The implementation of AI in DevOps can provide vast in a great spectrum. The CI/CD of any software applications was completely triggered to a whole new level. The limitations in identifying any defects/bugs and resolving them were totally obliterated, as the AI-based system was capable of intelligently monitoring the entire software process lifecycle and immediately altering if there were any issues.
5 ways AI is transforming DevOps:
The implementation of AI completely transformed DevOps. The following points list out some area where AI is transforming the DevOps team:
1. Advanced Automation:
DevOps itself has an automation process where it can automate the process of CI/CD with the aid of multiple tools. But with AI, things get a little more advanced. For enabling automation to the next level in DevOps, AI contributes to a great extent. With the help of AI, reducing manual intervention across processes is made possible. That is, AI-based systems are capable of completely handling out every process of software integration and deployment. This eventually assists in enhancing end-user satisfaction, as the software is well tested and deployed at a more accelerated pace.
2. Better OpsData Correlation:
As the enterprise becomes more extensive, there is a surfeit use of development and deployment environments. Data correlation is one of the major parts of enterprise IT. A huge volume and variety of data are generated from multiple sources in KPI and logs. The AI-based systems will intelligently provide the correlation among OpsData. In this way, manually understanding and process every data is not needed. By applying AI, we can easily derive deep insights from these data supporting the DevOps team to understand the environment's overall health easily.
3. Quick Fix of Defects/Bugs:
The occurrence of defects/bugs is always high in any software application. However, the challenge here is to uncover these issues at the time they occur. That is not persisting them to become a more prominent issue. AI-based automation helps in identifying issues at the time they occur. The system not just identifies, it also helps in auto-remediating the problem. In this way, the AI-based systems can automatically stabilize the software before any impact on the end-user side.
4. Efficient Anomaly Detection Mechanism:
Anomaly detection is a challenge in most enterprise IT. Identifying unknown problems from huge volumes of data was a very crucial task. However, the implementation of AI made this task direct. AI-based models capable of ingesting structured and unstructured data, correlating them, and then identifying unknown problems and triggering them as anomalies data. In this way, problem tracing was made more accessible.
5. Intelligent Failure Prediction:
AI-based systems are capable of predicting the chance for the failure of any application. AI-based models can understand analyzing specific patterns from data and predicting if there is a chance of failure. If the occurrence is found, it will be intelligently altered, and the IT team can pre-solve the problem before it impacts business.
To learn more about Algomox AIOps please visit our AIOps Platform Page.