Why is Azure DevOps Automation important in AI systems?

Why adopt Azure DevOps on AI Systems?

  • Quality continuous delivery — Deliver code to production with no manual intervention
  • Monitoring — AI-enabled integration & monitoring in CI-CD pipeline for higher visibility across azure-DevOps toolchain.
  • Enable on-demand testing at every quality gate within a DevOps pipeline for advanced debugging purposes.
  • Operations — Enabling self-debugging cloud application by moving from manual run to “operations-as-a-code.”

How does the Azure DevOps pipeline work?

  • Azure Boards: It helps manage software product development with customized tools throughout the SDLC. It also helps efficiently support a set of capabilities for agility, Scrum, dashboards, and integrated reporting.
  • Azure Pipelines: It helps automate the entire CI-CD pipeline from build to deployment stages for consistent and reliable workflow. With Microsoft Azure software like Microsoft-hosted Linux, macOS, and Windows, DevOps engineers easily manage hardware and VMs.
  • GitHub and Azure: With a GitHub account, developers move code to deployment in less interval of time. Also, Azure-based DevOps automation can be seamlessly integrated for deployments to Azure Kubernetes Services, Web Apps, SQL Database, Azure Functions, and more.
  • Azure Test Plans: It provides different kinds of testing, such as manual testing, user acceptance testing, exploratory testing, etc.
  • Azure Monitor: It helps store and manage data by maximizing visibility, performance, and availability of all applications across the DevOps pipeline.

How does Azure-based DevOps automation work on AI systems?

  • With AI-enabled Azure DevOps automation workflow, generate the greatest gains in productivity and accuracy. Also, Azure-DevOps’ development tools are used with supervised machine learning algorithms for quick response to code requests in real-time.
  • Azure DevOps teams use AI and ML-powered requirements management platforms that help understand requirements with accuracy and quality at the right time that help develop an app or platform in the next generation.
  • Azure DevOps team using AI-enabled bug detection tool (CodeQL), which helps in faster bug detection and auto-suggestions for improving code.
  • In the Azure-enabled DevOps pipeline, AI helps in auto-generating and auto-running test cases based on the unique attributes of a given codebase by improving overall product quality.
  • At a leading supply chain management (SCM), Azure-DevOps teams use AI to analyze how some projects deliver excellent codes faster to market. This helps projects in gaining analytics insights into their data.

What are the Azure-based DevOps pipeline tools for AI systems?

  • Source-control repository: The DevOps team stores, manage and controls all framework, codes, and other AI pipeline artifacts throughout the CI-CD pipeline. It is also a collaboration hub, where pipeline artifacts are shared and used for development and product operations.
  • Data lake: Here, the DevOps team store, aggregate, and create data for use in framework and training throughout the AI DevOps (CI-CD) pipeline. It helps store multi-structured data by AI developers to facilitate data used in the models.




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