Ensuring Reproducibility in Machine Learning Systems

Reproducibility is a foundational requirement for reliable ML systems, enabling debugging, collaboration, and regulatory compliance. Key best practices include versioning code and data with tools like Git and DVC, fixing random seeds for determinism in frameworks like TensorFlow and PyTorch, tracking experiments with MLflow, Show more