When writing machine-learning pipelines, you have a lot of decisions to make, such as picking transformers, estimators, and hyperparameters. Since some of these decisions are tricky, you will likely find yourself searching over many possible pipelines. Machine-learning automation tools help with this search. Unfortunately, each of these tools has its own API, and the search spaces are not necessarily consistent nor even correct. We have discovered that types (such as enum, float, or dictionary) can both check the correctness of, and help automatically search over, hyperparameters and pipeline configurations. This talk presents Lale, an open-source Python library for semi-automated data science. Lale is compatible with scikit-learn, adding a simple interface to existing machine-learning automation tools. Lale lets you search over possible pipelines in just a few lines of code while remaining in control of your work.