Collection of scripts to implement MLOps using Azure Machine Learning.

• The production code is hosted in the main branch.
• A data scientist creates a feature branch for model development.
• The data scientist creates a pull request to propose to push changes to the main branch.
• When a pull request is created, a GitHub Actions workflow is triggered to verify the code.
• When the code passes linting and unit testing, the lead data scientist needs to approve the proposed changes.
• After the lead data scientist approves the changes, the pull request is merged, and the main branch is updated accordingly.