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Machine learning, big data analytics and other AI workloads have traditionally utilized the map-reduce model of computing where data is local to the compute jobs.
However, in cloud-native environments, storage must become stateless, elastic, and be able to scale independently of the compute systems to effectively accommodate workload fluctuations.
Download this paper to discover one system that is designed for AI and ML workloads and is equipped to do so, the MinIO high-performance object storage server, and to determine how your environments may benefit from a similar architecture.