<ul><li>The Google Cloud Storage team is focused on providing tools to optimize AI/ML workloads.</li><li>AI/ML data pipelines involve data preparation, model training, and model serving which can stress storage systems.</li><li>Cloud Storage's hierarchical namespace (HNS) can enhance performance and efficiency of AI/ML workloads.</li><li>HNS enables atomic and fast folder renames, aiding in checkpointing and fault tolerance.</li><li>Hierarchical namespace offers benefits like optimized storage layout for higher QPS of reads and writes.</li><li>Folders in HNS buckets allow for organizing data in a tree-like structure for efficient filesystem operations.</li><li>Using HNS leads to up to a 20x faster checkpointing speed compared to flat namespace buckets.</li><li>HNS buckets support up to 8x higher initial object read and write requests per second compared to flat buckets.</li><li>Companies like AssemblyAI have seen significant improvements using HNS with Cloud Storage FUSE for AI/ML workloads.</li><li>In conclusion, enabling hierarchical namespace on new buckets is recommended for AI/ML workloads.</li></ul>

Accelerate AI/ML workloads using Cloud Storage hierarchical namespace

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