Storing and Distributing AI Training Data Using Storj

This technical report discusses the processes involved in storing and distributing AI training data and models using Storj Cloud Object Storage. It outlines the challenges faced in the generative AI space, particularly regarding data transfer, storage, and distribution across decentralized networks. The report details the three main phases of the AI workflow: model training and creation, model customization and fine-tuning, and model execution and inference. It emphasizes the need for secure and efficient data distribution methods to manage large datasets across various geographic locations. Additionally, the document presents case studies on the distribution of the LAION training dataset and the StarCoder model, comparing performance metrics between Storj and traditional storage solutions like Amazon S3. The findings indicate that Storj offers significant advantages in terms of speed and cost-effectiveness for AI workloads, making it suitable for the evolving demands of distributed AI applications.