The article delves into the construction of a reproducible end-to-end machine learning pipeline using a Keras image multi-class classification model, focusing on a dataset sourced from Google Open Images, customized with Quilt T4 and comet.ml. It highlights the cyclical and iterative nature of machine learning pipelines, emphasizing the importance of data and model versioning for reproducibility. The tutorial demonstrates downloading a categorical subset of the Open Images corpus, specifically fruit images, and addresses class imbalance issues. It guides the reader through building a baseline model using a small CNN and then transitioning to a more effective approach with a pre-trained InceptionV3 model, illustrating the benefits of transfer learning. The tutorial also underscores the utility of comet.ml for tracking and logging experiments and results, ensuring that machine learning experiments remain accessible, trackable, and reproducible. The piece draws attention to tools like Quilt for data versioning and discusses methods for sharing models and data with collaborators, ultimately highlighting the synergy between Quilt and Comet in facilitating a seamless machine learning workflow.