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Three Ways That AI Will Impact Your
Data Management and Storage Strategy
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Gartner analysts provide a detailed review of the impacts on infrastructure when architecting AI workloads that involve machine learning and deep learning. Within this report, the analysts outline best practices for storage selection and implementation.
Key Points from this research:
- Choose vendors and products that can deliver high performance for both bandwidth-oriented batch workloads and small-file workloads, as most traditional solutions can’t deliver good performance for both sequential and random storage I/O.
- Use shared storage approaches to consolidate data platforms and eliminate movement between ML and DNN data pipeline stages, and to improve storage efficiency.
- Modernize existing storage networks by leveraging high performance fabrics. Consider implementing RDMA NVMe-oF protocols to improve performance when leveraing low-latency NVMe media.
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Gartner [Three Ways That AI Will Impact Your Data Management and Storage Strategy], Julia Palmer, Arun Chandrasekaran, Chirag Dekate, August 2018