The typical steps for building a new machine learning model for property prediction consist of first processing the molecules in a suitable representation, like a feature vector, and then applying machine learning-specific processing and algorithms to these features. Two widely employed packages are RDKit 7 and scikit-learn. RDKit can process molecules, including reading different input formats, filtering out unwanted compounds, standardizing molecules, and computing various molecular features. scikit-learn can use the molecular feature matrix generated with RDKit to perform feature selection or reduction, set up crossvalidation experiments, and train and select prediction models. While these are standard tasks, they can require manual work that can become repetitive, time-consuming, and errorprone. Manually handling these steps does not scale for large-scale evaluations, requiring robust automation of the process. Simultaneously, different projects might require different molecular standardization, custom data splits and featurization, and heterogeneous molecular data from various sources demands simple and flexible adaptation to the project's needs.