How to fulfill the storage needs of machine learning workloads
In order to make the proper inferences needed for analytics-driven insights, machine (ML) and deep learning (DL) workloads are required to process previously unheard of amounts of data. In fact, 80% of the time spent for AI is in data preparation.
So what’s the best way to handle all of that data?
Evaluator Group Senior Strategist Randy Kearns and VP of Marketing at WekaIO Barbara Murphy dig into this question in this custom webcast; they also examine a high-performance storage system that provides the parallel data access, architecture for multiple data types, and flexible scalability that ML and DL workloads require.
Tune in to learn how your organization can take advantage of a similar system.