Organization and storage of large datasets for ai training

The article examines current approaches to organizing and storing large datasets used for training artificial intelligence (AI) models, particularly for radio signal recognition tasks. It emphasizes the need for reliable and efficient data storage solutions due to the increasing scale and complexity of data involved in AI applications. The study analyzes various data storage formats (HDF5, WAV, IQ raw data), provides an overview of cloud-based solutions (AWS, Google Cloud, Azure), local storage systems (SAN, NAS, JBOD), and distributed file systems. It discusses best practices in data versioning and cataloging for enhanced accessibility, performance, and reproducibility of AI training processes. Recommendations are provided for selecting optimal storage methods based on the specific requirements of AI projects and the characteristics of the processed data.