Directions in MLOps: Exploring More Collaboration with GitHub Actions

Directions in MLOps: Exploring More Collaboration with GitHub Actions

MLOps and GitHub Actions series #6 | Wrap up!

Hello everyone! Here it is, the final article to conclude the Introduction to MLOps with GitHub Actions series! This series aims to be very beginner-friendly with low code for those who just wants to learn MLOps principles and apply it simply with GitHub Actions.

It's been super fun to publish an MLOps article a week for the last 6 weeks. I'm excited to have shared some knowledge about MLOps in this series.

As the field of machine learning continues to evolve, new trends and technologies are shaping the way we maintain, operate and scale machine learning apps. In this article, we'll explore more ways in which GitHub Actions can provide in supporting MLOps.

1. Kubernetes Orchestration for Scalability

According to edgedelta.com, "More than 60% of enterprises have adopted Kubernetes, and the Cloud Native Computing Foundation (CNCF) annual survey shows that the adoption rates of Kubernetes have increased to 96%."

Kubernetes has emerged as a popular platform for container orchestration, providing scalability, resilience, and automation for deploying and managing containerized applications. In the context of MLOps, Kubernetes enables efficient deployment and scaling of machine learning models in production environments, with features such as auto-scaling, resource allocation, and service discovery.

GitHub Actions provides native support for Kubernetes, allowing users to deploy containerized workflows and applications to Kubernetes clusters seamlessly.

2. Model Versioning and Artifact Management

Model versioning and artifact management are critical aspects of MLOps, enabling reproducibility, traceability, and collaboration in machine learning workflows.

GitHub Actions provides built-in support for version control and artifact management, allowing users to store, version, and share artifacts such as trained models, datasets, and experiment results directly within their GitHub repositories. Below is a simple workflow example to save a trained model as an artifact.

name: Save ML Model as Artifact

on:
  push:
    branches:
      - main
jobs:
  save-as-artifact:
    runs-on: ubuntu-latest

    steps:
    - name: Checkout repository
      uses: actions/checkout@v2

    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.8'

    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt

    - name: Train model
      run: python train_model.py
    # using the upload-artifact action to save the model
    - name: Upload model artifact 
      uses: actions/upload-artifact@v2
      with:
        name: trained-model  # Name of the artifact to save
        path: model          # Directory to save the artifact contents

This allows us to store the trained model and use it in another workflow for testing or later use. Here's how to download an artifact.

name: Use Artifact Example

on:
  push:
    branches:
      - main
jobs:
  download-artifact:
    runs-on: ubuntu-latest
    steps:
    - name: Checkout repository
      uses: actions/checkout@v2

    - name: Download artifact
      uses: actions/download-artifact@v2
      with:
        name: trained-model   # Name of the artifact to download
        path: model           # Directory to download the artifact contents

3. Automated Governance and Compliance

With the increasing adoption of machine learning in regulated industries such as healthcare and finance, there is a growing need for automated governance and compliance solutions that ensure models adhere to legal, ethical, and regulatory requirements.

GitHub Actions can be integrated to build organization-wide governance and compliance tools to enforce policies, perform audits, and monitor model behavior automatically. Organizations can use GitHub Actions to implement pre-deployment checks, post-deployment audits, and ongoing monitoring processes that ensure models comply with industry regulations and organizational standards.

Conclusion

The growth in establishing a robust MLOps pipeline highlights the evolving landscape of machine learning as a whole. I hope this series introduces the concepts of MLOps in a simplified manner for you. I hope you are now able to create and optimize your MLOps workflows to automate tasks with GitHub Actions!

To conclude this series, let me share additional resources for further exploration on the topic:

Thank you for joining me in this series! Do give a like, follow and stay tuned for more articles! Cheers!


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