Dask is particularly useful when you have a workflow that already heavily relies on NumPy or Pandas and you need to handle datasets that exceed your memory capacity. For example, if you are processing a large CSV file that does not fit into memory with Pandas alone, you can use Dask to work with it in chunks while retaining a familiar API. This allows for an easier transition without needing to rewrite your existing codebase.
You would use Ray over Dask when you need to perform distributed machine learning tasks that require low-latency and high-throughput. For example, if you're training a deep learning model that uses many GPUs across different nodes, Ray can manage resource allocation and scheduling more effectively due to its actor model and support for remote function calls.