Set up Git integration for source control

Git integration is an essential component of any software development project, including data science solutions. By leveraging Git for source control, you can effectively manage your codebase, collaborate with team members, and track changes to your data science projects. In this article, we will walk through the steps to set up Git integration for source control in the context of designing and implementing a data science solution on Azure.

Step 1: Create an Azure Machine Learning workspace

An Azure Machine Learning workspace provides a centralized location to manage your data science assets. If you already have a workspace, you can skip this step. Otherwise, follow the official Microsoft documentation to create an Azure Machine Learning workspace.

Step 2: Initialize a Git repository

Once you have a workspace, you can initialize a Git repository to enable source control for your data science projects. To do this, you can use the Azure Machine Learning SDK or the Azure Machine Learning studio.

Using Azure Machine Learning SDK:

1. Install the Azure Machine Learning SDK by running the following command:

pip install azureml-sdk

2. Open a Python script or Jupyter notebook and import the necessary libraries:

from azureml.core import Workspace, Experiment, VersionControlConfiguration

3. Load the workspace:

ws = Workspace.from_config()

4. Initialize the Git repository:

ws.initialize_git_repository()

Using Azure Machine Learning studio:

1. Open the Azure Machine Learning studio by navigating to your workspace in the Azure portal.
2. Click on “Repos” in the left sidebar.
3. Click on “Initialize repository” and follow the instructions to initialize the Git repository.

Step 3: Configure Git integration

After initializing the Git repository, you need to configure Git integration to enable seamless collaboration and version control for your data science projects.

Using Azure Machine Learning SDK:

1. Import the necessary libraries:

from azureml.core import Workspace, VersionControlConfiguration

2. Load the workspace:

ws = Workspace.from_config()

3. Get the version control configuration:

vc_config = VersionControlConfiguration.get(workspace=ws)

4. Configure Git integration:

vc_config.set_repository_configuration("git_url", "default_branch", "project_folder")
vc_config.save()

Replace “git_url” with the URL of your Git repository, “default_branch” with the name of the default branch (e.g., “main” or “master”), and “project_folder” with the path to the project folder within the repository.

Using Azure Machine Learning studio:

1. Open the Azure Machine Learning studio.
2. Click on “Repos” in the left sidebar.
3. Click on “Connect to external Git repository” and follow the instructions to configure Git integration.

Step 4: Clone the Git repository

Once the Git integration is configured, you can clone the Git repository to your local development environment. Cloning the repository will create a local copy of the codebase and allow you to make changes and contribute to the project.

Using Azure Machine Learning SDK:

1. Import the necessary libraries:

from azureml.core import Workspace, Experiment, VersionControlConfiguration

2. Load the workspace:

ws = Workspace.from_config()

3. Clone the Git repository:

repo = ws.get_default_repo()
repo.clone(".", overwrite=True)

Using Git command line:

1. Open a command prompt or terminal.
2. Navigate to the directory where you want to clone the repository.
3. Run the following command:

git clone

Replace “” with the URL of your Git repository.

Congratulations! You have successfully set up Git integration for source control in your data science solution on Azure. You can now commit and push changes to the remote repository, collaborate with team members, and track the history of your data science projects using Git.

Remember to regularly commit and push your changes to the remote repository to ensure that your work is backed up and easily accessible to others. Git integration provides a powerful version control mechanism that helps streamline collaboration and ensure the integrity of your data science solution.

In summary, Git integration is crucial for managing and tracking changes to your data science projects. By leveraging Azure Machine Learning workspace and Git, you can effectively collaborate, version control, and maintain the integrity of your codebase. Follow the steps outlined in this article to set up Git integration for your data science solution on Azure and start benefiting from the features provided by Git and Azure Machine Learning.