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What Is The Future of DevOps?
With the growing popularity of DevOps, many enterprises have started working on deploying DevOps strategies as a part of their software development process. Just like in any other field, technology advancements are happening rapidly in DevOps. As per the experts view, in the near future, the technical advancements in DevOps will be focusing on security & DevSecops, using AI/ML to improve processes, and more automation.
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DevOps Security
Currently, security is the major concern in DevOps. As DevOps strategies emphasize the need to automate the resources throughout the CI/CD pipeline, security issues are erupting more frequently. More connected = more exposure. So, the major question that arises here is that how would be the security concerns tackled when new tools comes out. To deal with these security issues, there has been a significant progress in DevSecOps.
DevSecOps is the latest advancement in DevOps that addresses the security and privacy issues. DevSecOps takes care of all the common security and privacy concerts & there’s nothing to worry about. The technical teams inside the DevOps work environment should be made aware of DevSecOps.
Over the coming years, new technologies will be integrated into DevOps like Public cloud & IoT. This integration will be addressing a lot of issues & there will be more discipline about security. So, over the coming years, we will be witnessing several advancements in DevOps in terms of security, IoT and cloud computing. With the integration of these technologies, enterprises that successfully deploy DevOps will be achieving several benefits in the event of rapid software delivery & shortened development lifecycle,
Artificial Intelligence & Machine Learning:
There is a plenty of scope for integration of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) into DevOps. These technologies can help the DevOps Engineers to get a clear insights to different aspects like What do I need to test? What is the impact of a change? Both AI and ML could also help in analyzing thousands of test results & accurately detect the failure rate.
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