Reproducibility in machine learning requires fixed random seeds, deterministic operations, pinned dependencies, and thorough documentation, enabling experiments to be replicated exactly and building confidence in reported results. Random seeds: set seeds for all random number generators (Python, NumPy, PyTorch, CUDA); ensures same initialization and data shuffling. Deterministic operations: some GPU operations are non-deterministic by default for speed; enable deterministic mode (torch.backends.cudnn.deterministic = True). Performance trade-off: deterministic operations may be slower; choose when reproducibility matters more than speed. Dependency pinning: record exact versions of all packages (requirements.txt, conda environment.yml); library updates can change behavior. Hardware considerations: different GPUs may produce slightly different results; document hardware used. Data version control: track exact dataset versions; DVC or similar tools for data versioning. Code versioning: commit code before experiments; tag or record commit hash with results. Experiment tracking: log hyperparameters, metrics, and outputs (MLflow, W&B, TensorBoard). Configuration files: parameterize experiments; same config should produce same results. Documentation: write down all steps to reproduce; include setup instructions. Challenges: some operations fundamentally non-deterministic; document known sources of variation. Reproducibility enables debugging, verification, and scientific progress in ML research.