Machine Learning Code Quality and Testing Questions

Writing testable and maintainable machine learning code with robust debugging practices. Topics include building clear abstractions, unit tests for data transforms and model components, integration tests for training and inference pipelines, traceable model artifacts, deterministic runs and seed control, logging and monitoring, and debugging techniques such as not a number checks, gradient and numerical stability checks, and data validation. Emphasize continuous integration and reproducibility for research and production code.