What is in store for DevOps Future?

Artificial intelligence is the talk of the town, and the craze has no bounds at the moment. The leaps in AI/ML technology are exciting as well as essential for the way the financial industry is evolving. The complexity of the financial systems with their explosive data calls for the use of technology to make it usable. But that’s just the operational front of the trade. Predictive analysis for software development is a more specific use case of AI.

Predictive analysis can help improve the SDLC through a feedback loop mechanism that flags risk-prone areas. The deviations in the production environment can be monitored by comparing the system’s desired and current states, as in the case of GitOps. As the development process is becoming overly automated and the server loads are increasing, it has become vital to track any anomalous behavior in the systems to plug the system errors in the very beginning. The longer these are left to linger, the harder they become to weed out, and the financial distress caused by the time is also enormous. Predictive analysis also helps stay clear of alert storm floods, saving time and resources.

Application delivery can also be observed for any discrepancies in the workflow through DevOps tools like Ansible and Puppet. Appropriate surveillance is the key to intelligent automation, and the same can be achieved via machine learning tools. Software development quality can also be improved through an ongoing testing library that fixes bugs at lightning speeds and keeps adding to the AI’s learning curve. Even security breaches can be identified and stopped beforehand via digital fingerprints of system usage. This is not only punitive but also an excellent deterrent for malicious activities. Sadly, 33% of the ESG surveyed organizations admittedly lacked AI/ML skills, which makes the predictive analysis trend even more prominent in the time to come.