Source: thenewstack.io
The Architect’s Guide to Storage for AI
Choosing the best storage for all phases of a machine learning (ML) project is critical. A well-trained model running in production is what adds AI to an application, so this is the ultimate goal.
However, collectively, these teams may put a considerable storage requirement on a central storage solution.
Today, these options fall into the following categories: local file storage, network-attached storage (NAS), storage-area networks (SAN), distributed file systems (DFS) and object storage.
Local file storage: The file system on a researcher’s workstation and the file system on a server dedicated to model serving are examples of local file systems used for ML storage.
However, collectively, these teams may put a considerable storage requirement on a central storage solution.
Today, these options fall into the following categories: local file storage, network-attached storage (NAS), storage-area networks (SAN), distributed file systems (DFS) and object storage.
Local file storage: The file system on a researcher’s workstation and the file system on a server dedicated to model serving are examples of local file systems used for ML storage.
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