Production Ready Data Science
Production Ready Data Science
From Prototyping to Production with Python
About the Book
This comprehensive guide bridges the gap between data analysis and software engineering, providing you with the essential tools and best practices to transform your data science projects into scalable, maintainable, and collaborative solutions. Through practical examples and clear explanations, you’ll master techniques for:
- Transform messy notebooks into organized, maintainable code
- Create reproducible environments across teams and deployments
- Write modular, reusable, and testable Python code
- Implement robust data validation and error handling
- Leverage version control for code and data integrity
- Implement automated testing to catch bugs early
- And much more!
Whether you’re a data scientist seeking to elevate your projects, a machine learning engineer building production-grade models, or a developer venturing into data-driven applications, this book is your comprehensive guide to engineering high-quality, reliable data science solutions.
About the Author
Table of Contents
- Preface
- Motivation
- Audience
- Prerequisites
- What Makes This Book Different
- About the Author
- Copyright
- 1. Version Control
- 1.1 What Is Version Control?
- 1.2 Why Is Version Control Essential?
- 1.3 Use Git for Version Control
- 1.4 Best Practices in Version Control
- 1.5 Key Takeaways
- 2. Dependency Management
- 2.1 What Is Dependency Management?
- 2.2 Best Practices for Dependency Management
- 2.3 Use uv to Manage Dependencies
- 2.4 Key Takeaways
- 3. Python Modules and Packages
- 3.1 What Are Python Modules and Packages?
- 3.2 Project Organization Best Practices
- 3.3 Import Best Practices
- 3.4 Key Takeaways
- 4. Python Variables
- 4.1 What Are Variables?
- 4.2 Choose the Right Python Collection
- 4.3 Best Practices for Python Variables
- 4.4 Key Takeaways
- 5. Python Functions
- 5.1 What Are Python Functions?
- 5.2 Why Are Python Functions Essential?
- 5.3 Best Practices for Python Functions
- 5.4 Advanced Function Toolkit
- 5.5 Key Takeaways
- 6. Python Classes
- 6.1 What Are Python Classes?
- 6.2 Best Practices for Python Classes
- 6.3 Advanced Class Toolkit
- 6.4 Key Takeaways
- 7. Unit Testing
- 7.1 What Is Unit Testing?
- 7.2 Why Is Unit Testing Essential?
- 7.3 Use Pytest for Unit Testing
- 7.4 Best Practices for Unit Testing
- 7.5 Key Takeaways
- 8. Configuration Management
- 8.1 What Is Configuration Management?
- 8.2 Why Is Configuration Management Essential?
- 8.3 Use Hydra to Manage Configurations
- 8.4 Best Practices for Configuration Management
- 8.5 Key Takeaways
- 9. Logging and Exception Handling
- 9.1 What Is Logging?
- 9.2 Why Should You Use Logging Instead of Print?
- 9.3 Use Loguru for Python Logging
- 9.4 Best Practices for Exception Handling
- 9.5 Key Takeaways
- 10. Data Validation
- 10.1 What Is Data Validation?
- 10.2 Why Is Data Validation Essential?
- 10.3 Data Validation Made Easy with Pandera
- 10.4 Best Practices for Data Validation
- 10.5 Key Takeaways
- 11. Data Version Control
- 11.1 What Is Data Version Control?
- 11.2 Why Is Data Version Control Essential?
- 11.3 Use DVC for Data Version Control
- 11.4 Key Takeaways
- 12. Continuous Integration
- 12.1 What Is Continuous Integration?
- 12.2 Why Is Continuous Integration Important?
- 12.3 Use GitHub Actions for Continuous Integration
- 12.4 Common Data Science Workflows
- 12.5 Key Takeaways
- 13. Package Your Project
- 13.1 What Is Packaging?
- 13.2 Why Is Packaging Essential?
- 13.3 Use uv for Packaging
- 13.4 Manage Package Versions
- 13.5 Add a Documentation Page
- 13.6 Key Takeaways
- 14. Notebooks in Production
- 14.1 Notebook Production Challenges
- 14.2 Best Practices for Jupyter Notebooks
- 14.3 Use marimo for Reproducible Data Science
- 14.4 Key Takeaways
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