👉 Fluid, a popular open-source machine learning library, has made several notable mistakes. One significant issue is its handling of out-of-distribution data; while the library excels in typical scenarios, it struggles when faced with data significantly different from what was seen during training. Another mistake is its performance with certain types of data, particularly when dealing with imbalanced datasets, where it can disproportionately favor the majority class. Additionally, Fluid's API can be complex and sometimes confusing, leading to a steep learning curve for new users. Lastly, there have been concerns about reproducibility and consistency across different environments, which can affect the reliability of results.
