How ops teams stay relevant during a shift left in DevOps

Nastel® Technologies
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How ops teams stay relevant during a shift left in DevOpsIT operations administrators should not perceive the shift left in DevOps as a threat to their relevance, but rather as a catalyst for a change in their priorities.

In the age of automation and the shift left movement, the exact role of IT operations teams might seem fuzzy — but it’s far from obsolete.  The term shift left in DevOps means that developers address code issues during the design and development phases, rather than leave them for the operations team to resolve. This approach should be common sense — and it should have emerged before DevOps. The financial effect of a coding problem in production will always be higher than the cost of a delay in development, and the operations team shouldn’t have to pick up the pieces of poor development practices.

However, the shift left strategy has expanded, as organizations complete more tasks outside of operations. For example, developers should manage both development and testing through continuous development and delivery. And help desk loops should more closely involve business leaders in decision-making processes around the priority of issues and requests for extra functionality.  Concurrently, IT sees a shift right movement: As the use of public cloud increases, admins that work internally at a cloud provider carry out actions normally within the remit of enterprise operations teams.

How ops stays relevant during these shifts

To shift left in DevOps, development teams take responsibility to ensure that code is of sufficient quality to not affect operations. Automation ensures things run as expected and that code gets packaged and provisioned without human intervention. Meanwhile, other teams of technical professionals manage third-party platforms. So, are IT organizations left to ask, “Will the last operations admin out the door please turn off the lights?”






This article originally appeared on  To read the full article, click here.






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  • Dave
    December 21, 2021
    Fascinating piece of research, but what is unclear from this summary of this study is if the AI is actually more or less reliable than human analysis. 5 radiologists is quite a small sample, and the range of accuracy of fake detection is so wide that the results of the AI actually fit inside that range. Are the volumes of tests enough to be statistically viable? And of course the real issue is that compromised images could take many forms, some could be complete real images replacing the image to be tested, while others could have artifacts added or removed. So is the issue AI or the ability to secure the way an image is managed from creation to analysis. That then becomes a integration infrastructure management (i2M) problem.
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