This repository contains resources and examples related to Machine Learning Operations (MLOps), focusing on the principles of Continuous Delivery and Continuous Training (CI/CD/CT) for machine learning systems.
The goal of this project is to provide practical examples and guidance on building robust and automated MLOps pipelines. The content explores concepts discussed in modern MLOps architectures, helping you understand how to deploy, monitor, and retrain machine learning models effectively.
The diagram below illustrates a typical MLOps architecture, similar to the concepts you'll find explored in this repository.
This repository is primarily composed of:
- Jupyter Notebooks (
.ipynb): Interactive notebooks that walk through various MLOps tasks, from data preprocessing and model training to pipeline automation. - Python Scripts (
.py): Helper scripts and modular code for use in the MLOps workflows.
To get started with the examples in this repository, you'll need to have Python and Jupyter installed.
-
Clone the repository:
git clone https://github.com/zerotrustprivacy/machine-learning.git cd machine-learning -
Install the required dependencies: It's recommended to create a virtual environment first.
pip install -r requirements.txt
(Note: A
requirements.txtfile should be added to specify project dependencies.) -
Launch Jupyter Notebook:
jupyter notebook
You can now navigate to the notebooks and run them.
For a deeper dive into the concepts of building data pipelines for MLOps, check out this blog post:
Additionally, the following Google Cloud article provides a comprehensive overview of MLOps automation pipelines:
Contributions are welcome! If you have suggestions for improvements or want to add more examples, please feel free to open an issue or submit a pull request.
This project is open source and available under the MIT License.