Software systems that learn from data with machine learning (ML) are used in critical decision-making processes. Unfortunately, real-world experience shows that the pipelines for data preparation, feature encoding and model training in ML systems are often brittle with respect to their input data. As a consequence, data scientists have to run different kinds of data centric what-if analyses to evaluate the robustness and reliability of such pipelines, e.g., with respect to data errors or preprocessing techniques. These what-if analyses follow a common pattern: they take an existing ML pipeline, create a pipeline variant by introducing a small change, and execute this pipeline variant to see how the change impacts the pipeline’s output score. We recently proposed mlwhatif, a library that enables data scientists to declaratively specify what-if analyses for an ML pipeline, and to automatically generate, optimize and execute the required pipeline variants. We demonstrate how data scientists can leverage mlwhatif for a variety of pipelines and three different what-if analyses focusing on the robustness of a pipeline against data errors, the impact of data cleaning operations, and the impact of data preprocessing operations on fairness. In particular, we demonstrate step-by-step how mlwhatif generates and optimizes the required execution plans for the pipeline analyses. Our library is publicly available at: https://github.com/stefan-grafberger/mlwhatif