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What do Ray, Horovod, Spark, and Dask do, and when do you use each for distributed ML?

These four get conflated constantly, but they live at different layers: data processing, distributed training, and general orchestration. The signal is matching the tool to the workload instead of reaching for the one you know. Here is the answer.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

These four get conflated constantly, but they live at different layers: data processing, distributed training, and general orchestration. The signal is matching the tool to the workload instead of reaching for the one you know. Here is the answer.

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