Data Bottleneck

Data bottlenecks in machine learning and related fields refer to limitations in data processing speed, efficiency, or availability that hinder model training and performance. Current research focuses on mitigating these bottlenecks through various strategies, including optimizing data loading and transfer (e.g., using specialized libraries like FFCV), designing hardware-friendly model architectures (e.g., lightweight CNNs for embedded systems), and improving data representation and communication (e.g., via dynamic vector quantization). Overcoming data bottlenecks is crucial for advancing machine learning applications, enabling faster training, improved model accuracy, and efficient deployment across diverse hardware platforms and data-intensive tasks.