Worked on enhancing the TorchRec tutorial within the pytorch/tutorials repository, focusing on distributed training concepts for recommendation systems. Updated the tutorial to align with the latest TorchRec library version, removing an outdated inference section to streamline the content and emphasize best practices in distributed workflows. Leveraged Python and PyTorch to ensure the tutorial accurately reflected current capabilities, improving clarity and maintainability for users exploring distributed machine learning techniques. The changes aimed to reduce confusion for new adopters and support reproducible experimentation with TorchRec. This work contributed a targeted feature update, demonstrating depth in distributed training and recommendation system technologies.