Oussama-KIASSI / Data-science-best-practices

This repo groups a set of MLOps best practices

Data Science best practices

This repository provides comprehensive guidance for Python and MLOps (DataOps, DevOps, and ModelOps) best practices. These practices collectively aim to enhance efficiency, maintainability, scalability, and reliability across data-centric projects.

Contents

1. Python best practices
2. MLOps best practices

References

Databricks

• MLOps
• MLOps workflows on Azure Databricks

Azure Machine Learning

• MLOps: Model management, deployment, and monitoring with Azure Machine Learning
• MLOps with Azure Machine Learning

Python

• PEP 8 – Style Guide for Python Code
• PEP 257 – Docstring Conventions
• Code Style — The Hitchhiker's Guide to Python
• styleguide - Style guides for Google-originated open-source projects

Usage

Feel free to explore each section and incorporate the recommended practices into your Data Science projects to ensure efficiency, maintainability, scalability, and reliability.

Contributing

Contributions are welcome! If you have any suggestions, improvements, or additional best practices to share, please open an issue or submit a pull request.

License

This repository is licensed under the CC0 1.0 Universal.