Hugging Face undertook an ambitious reproducibility study, attempting to reproduce 2,200 papers from the International Conference on Machine Learning (ICML) to assess the state of reproducibility in modern machine learning research. The effort revealed significant challenges in the ML research community's approach to documentation, code release, and experimental rigor. The findings highlight systemic issues that affect the reliability and integrity of published research. The reproducibility crisis in machine learning mirrors similar concerns in other scientific fields, where the inability to replicate published results undermines confidence in the research. The Hugging Face study identifies specific barriers that prevent replication attempts, from missing source code and inadequate hyperparameter documentation to computational cost constraints. These findings have implications not only for academic research standards but also for companies relying on published results to guide product development and research investments. The project serves as a call to action for the ML community to adopt stronger practices around code release, documentation, and open science. Hugging Face's initiative provides concrete data about where reproducibility breaks down, offering researchers and institutions guidance on how to improve their own work and strengthen the foundation of ML research.