Heavy Undocumented Preprocessing

Heavy undocumented preprocessing in machine learning pipelines significantly impacts computational efficiency and reproducibility, hindering both research progress and practical applications. Current research focuses on accelerating preprocessing through hardware acceleration (e.g., FPGAs), algorithmic optimizations (e.g., adaptive radius culling, parallel processing), and deep learning approaches (e.g., using CNNs for image preprocessing). Addressing this bottleneck is crucial for scaling machine learning to larger datasets and enabling real-time applications across diverse fields, from neuroimaging and medical image analysis to video processing and natural language processing.