CML MLOps Template 🤖🔬📦

This repository serves as a template for integrating CML (Continuous Machine Learning) into your MLOps workflow. CML allows you to automate and standardize machine learning workflows using for example GitHub Actions. 🚀💻

Introduction 📄🚀

Continuous Machine Learning is a framework for automating machine learning workflows, including data preprocessing, model training, evaluation, and deployment, all within a Git and GitHub environment. By using CML, you can ensure that your machine learning pipelines are reproducible, well-documented, and collaborative. 🧪🔍💼

In this repository, we demonstrate how to use CML in the context of MLOps. You'll find an example machine learning workflow using Python and popular libraries such as scikit-learn. The workflow consists of data preprocessing, model training, model evaluation, and CML-based reporting. 🐍📊📈

Getting Started 🛠️🏁

To use this template for your MLOps project, follow these steps:

1. Create a new repository from this template:

   • Click the "Use this template" button at the top of this page. 🗳️
   • Follow the prompts to create your new repository based on this template.

2. Clone your new repository:

   git clone https://github.com/yourusername/your-mlops-repo.git
   cd your-mlops-repo
   

3. Add a trigger event to the .github/workflows/pipeline.yaml:

   • Open .github/workflows/pipeline.yaml
   • Add a workflow trigger event (e.g. push) into line two

4. Commit and push your changes

CML Reporting 📢📄

CML is integrated into this template using GitHub Actions. The pipeline.yaml workflow file in the .github/workflows directory specifies how to run CML commands to create reports and visuals. You can customize this file to generate different types of reports, publish them as GitHub Actions artifacts, and notify your team about the workflow's status. 📊🚀

For more information on how to use CML, refer to the CML documentation. 📚

License 📜

This repository is provided under the MIT License. Feel free to modify and use it for your MLOps projects. 📄📇

Acknowledgments 🙏👏

This template is inspired by the amazing work done by the CML community and the broader MLOps community. 🌟🌍

Feedback and Contributions 🤝📣

We welcome feedback and contributions to this template. If you have suggestions or improvements, please open an issue or submit a pull request. 🙌🛠️

Happy MLOps and CML integration! 🚀🧠📊🤖