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Businesses have rapidly embraced GitOps as a standard Kubernetes deployment strategy as it helps them deploy applications in the shortest possible time. This is enabled by Argo, the most popular GitOps tool for rapid deployments. Argo Rollout, a reliable Progressive Delivery tool for Kubernetes, provides deployment strategies like blue-green and canary deployments. Thus, with GitOps, DevOps can store all the definitions as code in a Git repository and easily control and audit versions. Most importantly, it allows them to quickly roll back to the previous version and offers more control. Unfortunately, teams must ensure that they can do this safely.

Problems Associated with Release Verification

In continuous delivery (CD), when DevOps professionals make multiple updates to production environments with zero downtime, they often overlook the risks of releasing software into production. For instance, the risk assessment process in Argo CD may not be efficient as Argo Rollout cannot identify quality regression mainly because Argo uses monitoring tools such as Prometheus, DataDog, NewRelic, and Wavefront for verification metrics. For metric analysis, Argo uses an open-source service called Kayenta. Secondly, the status quo does not allow DevOps to triage the release process risks and resolve production issues. And most importantly, there is no provision to check for abnormalities in production after deployment. As a result, if something goes wrong, DevOps does not have the opportunity to reduce the impact and roll back quickly to the previous version.

To summarize, the problems associated with release verification in Argo are:

  1. Argo lacks an automated process that can identify quality regression.
  2. Inefficient/Manual process of analyzing or verifying release risks and troubleshooting them.
  3. Lack of policy and compliance automation.

Why Automate Deployment Verification?

This is the main reason DevOps teams need intelligent automation of the software delivery workflow. Incorporating AI and machine learning can ensure that the release workflows improve over time, resulting in continuous improvements in reliability, software quality, and risk reduction. Not only can they make Argo more effective by applying intelligent automation to deployment verifications but also further enhance software delivery practices when using advanced deployment strategies such as the blue-green, canary, and progressive deployments with Argo Rollouts.

If you or your team is rolling out applications into Kubernetes using Argo CD, be sure to watch the free webinar where we discuss how you can roll out safely into any environment. These include private cloud (OpenStack, OpenShift, Kubernetes, Mesos) or Public Cloud (AWS, GCP, Azure). You can integrate Argo rollouts with OpsMx Autopilot, an intelligent continuous verification platform for any CI/CD that helps organizations deliver apps into production without any performance and quality risk. 

Figure- Architecture of how Autopilot adds deploy intelligence to Argo rollouts

Autopilot implements machine-learning-based analysis to intelligently verify new software releases for suitability in production – including reliability and business impact. As a result, you can minimize errors easily during the build, deploy, test, and release process. Plus, it eliminates the manual verification process and automated decisions for faster roll-back or roll-forward. Additionally, in case things go wrong, Autopilot offers advanced diagnostics features and triage of issues for immediate remediation.

If you missed our on-demand webinar Secure Application Deployment with Argo and Spinnaker, you can watch the recorded event online. 

Register Now!

Anushua Gorai

Anushua is a Sr. Content Marketing Specialist at OpsMx with over 8 years of experience. Her background is in B2B SaaS content marketing. When not at work, she likes cooking for her family, spending time with her pet, propagating plants, working out at the gym, and eating carbs.

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