A Data Scientist's Guide to Unit & End-to-End Testing

This talk provides a comprehensive guide for data scientists on unit and end-to-end testing of their developed and deployed models. It covers the importance of testing in ML, Test-Driven Development (TDD), and tools for testing ML models like `unittest`, `pytest`, `drone`, and `GitHub Actions`.

The presentation delves into:
- **Unit Testing**: Verifying individual components, best practices, and practical examples using `pytest`.
- **End-to-End (E2E) Testing**: Validating the entire ML workflow, dependency injection for flexible test configurations, and best practices.
- **CI/CD Integration**: Automating model testing and deployment.

It is ideal for data scientists, machine learning engineers, and anyone developing and deploying ML models to enhance their testing practices.