The current scenario in the automated testing area is riddled with hurdles and hindrances, despite the presence of an automated workflow.

Coming to the subject of DevOps testing, it is undoubtedly a revolutionary way forward for a streamlined business process, however, there are few challenges present in this future-forward process as well.

Managing resources – defining, researching and executing a project; managing tools- selecting the right tools, choosing agile or traditional methodologies; managing costs- Implementing DevOps keeping ROI in mind; selecting the right testing environment.

Machine learning has been proven to reduce costs of testing and improve speeds significantly. ML can optimize your testing process by controlling end-to-end flow of the process. This relates to pre-building the test, modifying test data, reorganising the procedure and customising the test, with low or no code required!

ML not only helps in stabilizing the current test automation but also helps in creating a steady workflow process for future testing as well. AI/Ml can instantaneously recognize gaps in testing automation and resolve these issues, during the process itself.

It’s ‘self-healing’ characteristic makes it ideal for applications that require core decision making with little to no supervision from humans.

Machine Learning can be particularly handy in providing the support and attention to a sensitive workflow process such as Continuous Integration/ Continuous Development ( CI/CD ) as they need monitoring at each and every step.

Giving birth to a whole new process known as MLOps. MLOps is the next step towards a holistic approach to DevOps testing, enhanced with AI/ ML tools. Since CI/CD have become increasingly necessary for the DevOps process.

MLOps with the power of CI/CD are proven to give rise to faster testing processes and more accurate results as it detects more bugs in the testing process compared to any other form of testing.

Ultimately resulting in healthier team development and product development and more importantly, the most successful way to carry out product deployment.

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