The infrastructure required to train, deploy and scale AI from pilot project to value generating production environment is complex. Among many things, the infrastructure must be able to quickly process massive data sets at high speeds and ingest the broadest range of data. In addition, the computation need varies along the whole process of training and deploying. Traditional computing infrastructure helps in some stage, but lags in speed.Data is everywhere. The total data volume transferred increases at an exponential pace; the yearly worldwide data usage went from 2 Zeta Bytes in 2010 to more than 70 Zeta Bytes in 2021.Using DeepSquare computing Infrastructure and its marketplace. AI companies can train, iterate and deploy their AI models using a single cloud computing infrastructure provider. These reduce down the complexity, and cost attached with end-to-end solution for AI. Using such infrastructure, AI companies can differentiate through faster implementation, smarter insights and focus on pure innovation.
