ML Model Governance Using GitLab Merge Request Approvals

GitLab Project Management with Pulumi AI

If you're leveraging GitLab as a part of your machine learning (ML) operations (MLOps) workflow to enforce governance for ML models, one way to do so is by implementing a merge request approval process. By defining approval rules in your GitLab repository, you can ensure that changes to ML models (for example, updates to training algorithms, model parameters, or datasets) are reviewed and approved by designated team members or role holders before being merged into the production codebase. This can help maintain code quality, model integrity, and regulatory compliance.

In GitLab, you can control the merge request approval process using the ProjectApprovalRule resource. This resource allows you to specify certain criteria for merge request approvals, such as the number of required approvals or which individuals or groups are eligible to approve merge requests.

Below is a Pulumi program that sets up a GitLab repository with a merge request approval rule using the GitLab provider. This rule requires that any changes made to the master branch receive at least two approvals before they can be merged. Additionally, we specify that the approvals cannot come from the author of the merge request.

import pulumi
import pulumi_gitlab as gitlab

# Create a new GitLab project (repository).
project = gitlab.Project("ml-model-governance",
    name="ml-model-governance",
    description="A repository for ML model governance with merge request approvals."
)

# Define the merge request approval rule for the project.
approval_rule = gitlab.ProjectApprovalRule("mr-approval-rule",
    project=project.id,
    name="Mandatory Review Approval",
    approvals_required=2,  # Set the number of required approvals.
    rule_type="regular",  # This is a regular rule (not a code owner rule).
    user_ids=[1234567, 7654321],  # IDs of the users who can approve.
    # Uncomment the following line to restrict approvals to these users for a specific branch:
    # protected_branch_ids=[some_branch_id],
    merge_requests_author_approval=False  # Disallow the author from approving their own MRs.
)

# Export the URL of the GitLab project.
pulumi.export("project_url", project.web_url)

In this program:

We start by importing the necessary Pulumi libraries.
We then create a GitLab project, which is equivalent to a repository.
We define an approval rule for this project that specifies:
- approvals_required: The number of approvals needed for a merge request to proceed.
- rule_type: The type of approval rule, which is regular in this case.
- user_ids: This is an array specifying the user IDs of the individuals who can approve the merge requests.
- protected_branch_ids: By uncommenting this line and providing the protected_branch_ids, you can restrict approvals to certain branches (this is optional and should be used if you want to apply approval rules to specific branches).
- merge_requests_author_approval: A boolean that indicates whether the author of the merge request is allowed to approve their own request.

By adjusting these parameters, you can customize the approval process to match your specific requirements for model governance. This level of control ensures that model updates are thoroughly reviewed and meet the project's standards before being deployed.

Lastly, we export the project URL so it can be easily accessed after deployment.

This Pulumi program helps you codify the governance rules, making your ML model governance process reproducible and auditable. It's crucial in regulated industries or in any team that aims for high standards of accountability and reproducibility in their ML workflows.