Beta-RecSys an open source project for Building, Evaluating and Tuning Automated Recommender Systems. Beta-RecSys aims to provide a practical data toolkit for building end-to-end recommendation systems in a standardized way. It provided means for dataset preparation and splitting using common strategies, a generalized model engine for implementing recommender models using Pytorch with a lot of models available out-of-the-box, as well as a unified training, validation, tuning and testing pipeline. Furthermore, Beta-RecSys is designed to be both modular and extensible, enabling new models to be quickly added to the framework. It is deployable in a wide range of environments via pre-built docker containers and supports distributed parameter tuning using Ray.