Distributed Machine Learning Model Training Pipeline

Design a Kubernetes-native distributed machine learning training pipeline for developing adaptive learning algorithms. Create a system that can parallelize model training across multiple GPU-enabled nodes, implement sophisticated hyperparameter tuning, and automatically version and track model performance. Develop comprehensive logging and tracking using MLflow, and create a reproducible deployment strategy for machine learning models in an educational context.