Extending DeepChem, AMPL prioritized automated machine learning for benchmarking and it enables users to conveniently build and validate models. However, it still lacks functionality to readily deploy and use models in practice. ZairaChem is another recent package, which proposes an automated cascade for training machine learning models, empowering users with little knowledge in data science to train robust ensemble-based models. However, one limitation of ZairaChem is that it currently only supports classification models and also does not enable model serialization with preparation steps included. This is addressed in a recently published package, PREFER , which wraps trained models fully, including data preprocessing. It implements a pipeline based on AutoSklearn , covering steps such as data preparation, model selection, and model evaluation. However, PREFER, offers a slightly less flexible API than the previous options and combining different feature representations and splitting methods is not possible without modifying the source code of the package itself.