The Fashion MNIST Dataset has been analyzed using Cleanlab Studio, an automated solution to find and fix data issues using AI. The audit revealed hundreds of erroneous labels and data issues in the dataset, which can affect product categorization and product identification efforts in e-commerce analytics and business intelligence. Mislabeling images, particularly those belonging to the t-shirt/top class, was a common issue, while footwear-type images were also found to be mislabeled. Additionally, Cleanlab Studio detected ambiguous examples and outliers that should be removed from the dataset entirely. The analysis highlights the importance of correcting data errors to train accurate models and draw reliable conclusions, and provides a tool for users to find and fix such issues in their own datasets.