This presentation introduces Tune and Fugue, frameworks for intuitive and scalable hyperparameter optimization (HPO). Tune supports both non-iterative and iterative HPO problems. For non-iterative problems, Tune supports grid search, random search, and Bayesian optimization. For iterative problems, Tune generalizes algorithms like Hyperband and Asynchronous Successive Halving. Tune allows tuning models both locally and in a distributed manner without code changes. The presentation demonstrates Tune's capabilities through examples tuning Scikit-Learn and Keras models. The goal of Tune and Fugue is to make HPO development easy, testable, and scalable.