AzureDevOps x AI-powered
Work starts in Azure Boards with tasks guiding the sprint. AI tools like GitHub Copilot help engineers by auto-generating documentation, suggesting project breakdowns, identifying risks, and offering real-time coding assistance in Azure Repos.
Our pipeline uses unit testing and SonarQube analysis with AI support after each pull request. AI predicts likely failures, analyzes past patterns, and reduces redundant tests, making quality control proactive. For NNG, this ensures higher reliability and fewer surprises in releases.
After review, Azure Pipelines CI/CD automates builds, pushes Docker images to Azure Container Registry, and deploys to Kubernetes clusters (Dev-Test, Staging, Production) with Helm charts. AI predicts failures, optimizes resources, and aids self-healing in Kubernetes, ensuring smooth releases from development to production.
Monitoring and feedback start at deployment, with Azure Monitor and Log Analytics capturing performance and health data. AI enhances this by detecting anomalies early, automating root cause analysis, and offering insights for future planning. This transforms monitoring into a strategic advantage, ensuring applications remain secure, stable, and improving.
Connecting NNG
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