What do Ray, Horovod, Spark, and Dask do, and when do you use each for distributed ML?
These four get confused constantly, but they sit at different layers: data processing, distributed training, and general orchestration. The signal is fitting the tool to the workload instead of reaching for the one you know. Here is the answer.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
These four get confused constantly, but they sit at different layers: data processing, distributed training, and general orchestration. The signal is fitting the tool to the workload instead of reaching for the one you know. Here is the answer.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
