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Machine learning projects fail more often from dependency conflicts than from model performance issues. A colleague’s training script crashes with cryptic NumPy errors. Your production deployment breaks because PyTorch installed a different CUDA version. A model that worked perfectly last month refuses to train after updating a single package. These scenarios plague ML teams daily … Read more