Fast Automatic ML Hyperparameter tuning Using Optuna (w. MLflow model registry and IRIS DB)

Short summary

Practical guide to automating hyperparameter tuning with Optuna, an MIT-licensed framework that efficiently explores parameter search spaces using TPE sampling and pruning. Shows how to integrate MLflow for experiment tracking and IRIS DB as a distributed backend, with working code examples on California Housing data. Includes decision rules for selecting cross-validation strategies.

• •Optuna automates hyperparameter search for scikit-learn, PyTorch, TensorFlow, and LightGBM models using TPE sampling and pruning strategies
• •MLflow tracks experiments and manages models; IRIS DB enables distributed optimization through shared storage
• •Tutorial includes decision trees for selecting cross-validation strategies and runnable Kaggle notebook

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