Hi @afdadfadsfadsf, Creating and managing model endpoints as part of your CI/CD pipeline is essential for deploying machine learning models. I can provide some guidance on how to set up a CI/CD pipeline using YAML in Azure DevOps.
You can adapt these principles to your specific use case.
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Azure DevOps YAML Pipelines:
- Azure DevOps allows you to define your build and release pipelines using YAML (YAML Ain’t Markup Language). This approach enables you to manage your pipeline configuration as code, just like any other source file.
- To get started, create a YAML build definition by adding a YAML file to the root of your repository. You can use the same pipeline features available in the visual designer but with a markup file.
- Here’s an example of configuring CI/CD pipelines as code with YAML in Azure DevOps:
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Sample ASP.NET Core Project with YAML Pipeline:
- If you’re working with ASP.NET Core, you can refer to this sample project on GitHub that includes a Core 2.2 web project and a sample YAML pipeline:
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GitLab CI/CD Minimal Example:
- While this example is from GitLab, the basic structure of a GitLab CI/CD pipeline defined in YAML can serve as a reference. It sets up a single-stage pipeline with one job:
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Amazon SageMaker CI/CD Pipeline:
- If you’re using Amazon SageMaker, this blog post demonstrates how to build a CI/CD pipeline for deploying custom machine learning models using AWS services:
Remember to adapt these examples to your specific MLflow deployment scenario. While YAML pipelines provide flexibility, you may need to customize the steps based on your requirements. Good luck with your CI/CD setup! 😊
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