1. Artifact Versioning for AI Pipelines Using GitHub


    Artifact versioning is a critical aspect of machine learning operations (MLOps), where you generate different versions of your models, datasets, and other artifacts as part of the AI pipeline. Version control systems like GitHub are essential for tracking changes, collaborating on code, and managing these artifacts.

    In a typical scenario, you could have a GitHub repository containing your AI pipeline code, including scripts for processing data, training models, and evaluating results. Each change to these scripts can be committed to the repository, creating a version history. Alongside the code, large files such as datasets or model binaries may be stored using Git Large File Storage (LFS).

    When you run your AI pipeline, each step could produce new artifacts. You might want to record which version of the code produced which artifact, and potentially push changes back to GitHub. This can be part of a Continuous Integration/Continuous Deployment (CI/CD) setup.

    Using Pulumi, you can automate the provisioning of infrastructure that supports artifact versioning using the pulumi_github provider. This provider allows you to create and configure GitHub resources such as repositories, branches, webhooks, and more through code.

    Below is a simple Pulumi program written in Python that creates a new GitHub repository where you could host your AI pipeline code and artifacts. This repository can be integrated into your AI pipeline to help manage versions of your artifacts.

    import pulumi import pulumi_github as github # Create a new GitHub repository to store AI pipeline artifacts ai_repository = github.Repository("ai_repository", # Set the name of the repository. name="ai-artifact-versioning", # Provide a description for the repository. description="Repository for storing AI pipeline artifacts and versioning", # Setting the repository to be private means it will not be viewable by the public. private=True, # Enable GitHub Issues for tracking and collaboration. has_issues=True, # Enable GitHub Projects for project management and tracking. has_projects=True, # Enable GitHub Wiki for documentation. has_wiki=True, ) # Export the URL of the created repository so that it can be used or accessed. pulumi.export("repository_url", ai_repository.html_url)

    In this program, we created a GitHub repository with the given attributes. It's set to private for security but includes issues, projects, and wiki features for collaboration. You can tie this into your AI pipelines using GitHub Actions or other CI/CD tools.

    This repository can be the starting point for your artifact versioning system. You may set up GitHub Actions within this repository to automate your AI pipelines. For example, every time you push your code, GitHub Actions can trigger a workflow that runs your AI pipeline, capture the version of the code, and store the resulting artifacts.

    Please ensure you have set up the required GitHub Personal Access Token and configured your Pulumi environment to use it. For more details on how to configure Pulumi with GitHub, you can refer to the pulumi_github provider documentation.

    To progress further, consider the following steps:

    1. Include more specific workflows in your .github/workflows directory for CI/CD.
    2. Use GitHub Releases to manage versions of your compiled artifacts.
    3. Utilize GitHub branches for managing stable and development versions of your code.
    4. Look into Git tags to mark specific commits that represent different versions of artifacts.

    By incorporating these techniques, you create a robust artifact versioning strategy leveraging GitHub's powerful collaboration and version control capabilities along with the infrastructure as code principles that Pulumi provides.