#continuous verification

The traditional model of authority For most of administrative history, government authority was exercised during an encounter. A person applied, presented evidence, and an official interpreted rules in context before reaching a decision. The law existed in statutes, but its meaning was realised through human judgement. Even complex bureaucracies preserved this structure. Decisions could be…

Sanjay Kumar Mohindroo Sanjay Kumar Mohindroo. skm.stayingalive.in Explore how Edge Security & Zero Trust Architecture with continuous verification secures distributed data and apps. Join the discussion!
 Quick insights to shift your security approach. Today, data and apps live everywhere. The old wall around the network no longer holds. We must shift to a model that checks every request at…

An OpsMx customer, a leader in the world of networking and cybersecurity solutions found it challenging to reduce build failures, and reduce the lead time to diagnose the root cause and fix them.

To mitigate this problem they had introduced a centralized build service for all developers that would facilitate a team of SREs for faster diagnosis and triage of errors and improve recovery rates.

Although this proved as a costly option the build error rates were still too high and time-consuming due to manual intervention. The customer was using Jenkins as the CI tool.

They chose the OpsMx Autopilot, an AI/ML-based Continuous Verification solution as their build verification tool. The Autopilot solution added a layer of intelligent visibility by gathering the build logs and using NLP and ML algorithms to decide whether to pass or fail a build and sending instant alert notifications to the concerned DevOps teams.

Automating the build verification process resulted in faster error identification and fixes, saved cost and time, and improved overall developer productivity.

Accelerate Innovation by Reducing Software Delivery Time

Faced with stiff competition, our customer needed to increase the speed of delivering enhancements to their end customers while simultaneously reducing production failures caused by defective updates.

The primary bottleneck they faced was a lengthy manual approval process to move updates from staging to production.  However, shortening the approval process had been proven to increase problems in production.

Their IT architecture is complex, aggravating the problem. They deploy a broad range of microservices-based applications on Kubernetes, as well as a large number of monolithic applications. Their CI/CD system was built using Jenkins, plugins, and custom scripts.

Moving more quickly was a key goal. They were able to process only 50 to 100 updates per month, and their goal was many hundreds of updates per month. Of course, like all companies, they are also under pressure to reduce costs. The process of verifying updates was estimated to have annual direct costs of more than $1M.

Every significant deployment is evaluated as they move to production. The analysis requires 3 expert engineers, including at least one technical lead and one product engineer, and it takes an hour or more to decide whether to move the deployment forward.