Setting Up GitHub for MLOps

Setting Up GitHub for MLOps

MLOps and Github Actions series #2 | Learn the ins and outs of MLOps

Welcome to back to article #2 of 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.

If you missed the first article, please check it out here. In this article, we'll walk through the process of creating a new repository, create a new workflow for GitHub Actions, and leveraging version control and collaboration features provided by GitHub to facilitate an effective MLOps pipeline.

Step 1: Creating a New Repository

  1. Sign in to GitHub: If you don't have an account, sign up for free. Once logged in, navigate to your dashboard.

  2. Create a New Repository: Click on the "New" button to create a new repository. Or if you have an existing project, feel free to use that repo instead.

Step 2: Create workflows folder

In our repository, create a .github/workflows/ folder. This directory is where you'll define your GitHub Actions workflows.

Each workflow will be in YAML format, specifying the actions to be executed and the triggers that initiate them.

MLOps Principles

Before we conclude this article, let's understand the key principles of MLOps. These will be useful in the next part of this series, where we will utilize GitHub Actions to implement these principles.

For those of you who are familiar with DevOps, MLOps is indeed an extension of DevOps principles and thus, follow similar principles.

  1. Automation: Automate repetitive tasks such as model training, evaluation, deployment, and monitoring to streamline the machine learning lifecycle and reduce manual effort.

  2. Collaboration: Foster collaboration and communication among data scientists, developers, and operations teams to ensure alignment, transparency, and shared understanding of project goals and requirements.

  3. Reproducibility: Ensure that machine learning experiments and workflows are reproducible by versioning code, data, and environment configurations. This enables others to reproduce results and troubleshoot issues effectively.

  4. Continuous Integration and Continuous Deployment (CI/CD): Implement CI/CD pipelines to automate the process of building, testing, and deploying machine learning models. This accelerates time-to-market, improves code quality, and enables rapid iteration and experimentation.

  5. Monitoring and Feedback: Continuously monitor model performance, data quality, and infrastructure metrics to detect issues, drift, and anomalies. Incorporate feedback loops to iteratively improve models and workflows over time.*t-1Y6c1ngIwRiHoMK6ElEQ.png

When is MLOps 'too much'?

After the first part of the series was published, where we discussed why MLOps is important, I received a question that asked when do we NOT need MLOps. Here are a few instances:

  • Small-Scale Projects: If you're just building a simple ML project that's easy to manage on its own, then it might not be worth to invest resources in MLOps.

  • Experimentation Phase: During the initial stages of development or experimentation, you would probably be iterating and testing ideas rather than investing time into unnecessary MLOps pipelines.

  • Not for Production: Models which are used for research or academic purposes and not intended for production would hardly need for MLOps.

  • Static Models: For models that do not require frequent updates or retraining, the benefits of MLOps may be minimal. These models can often be deployed and maintained with simpler processes.

  • Lack of Resources: For small teams/organizations with limited resources, setting up comprehensive MLOps pipelines might not be a priority.

  • Low Business Impact: Just like with every implementation decision, we need to look at costs versus impact, and whether investing in MLOps might be necessary.


Setting up your GitHub repository is the foundation for implementing MLOps practices in your machine learning projects. By following best practices for repository organization and leveraging GitHub's collaboration features, you can create a robust and efficient workflow that enables seamless collaboration, version control, and automation using GitHub Actions.

In the next article, we'll dive into the details of setting up CI/CD pipelines with GitHub Actions, automating tasks such as testing, linting, and model training to streamline your machine learning workflows.

Stay tuned for more insights and practical examples as we continue our journey into the world of MLOps with GitHub Actions. Cheers!

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