Evolving Storage for a New Generation of AI/ML

AI/ML is not new, but innovations in ML models development have made it possible to process data at unprecedented speeds. Data scientists have used standard POSIX file systems for years, but as the scale and nComputational storage can bring unique benefits in increasing the efficiency of CPU utilization in a data processing system. Here we discuss the benefits of leveraging computational storage in a disaggregated storage environment. We demonstrate the ability of the solution to complement the CPU by taking away tasks that benefit from in-situ processing within the storage, thereby improving the overall system performance while lowering the TCO. Disaggregated storage is particularly attractive when using computational storage since scaling storage naturally yields to scaling of tasks that can be accelerated using computational storage. We experimented with accelerating the S3 Select functionality using our disaggregated computational storage (DCS) platform. Data tagging and partitioning utilizing sharding aspect of DCS platform further enhances ability to provide even greater performance for large data processing with parallel execution.eed for performance have grown, many face new storage challenges. Samsung has been working with customers on new ways of approaching storage issues with object storage designed for use with AI/ML. Hear how software and hardware are evolving to allow unprecedented performance and scale of storage for Machine Learning.