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by sandGorgon
2179 days ago
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The tie breaker here really is kubernetes.
Most likely your company's infrastructure is run on k8s. As a data scientist you do not get control over that. Dask natively integrates with Kubernetes. That's why I see a lot of people moving away even from Apache Spark (which is generally used through its inbuilt scheduler YARN) and towards Dask. Second reason is that the dask-ml project is building seamless compatibility for higher order ML algorithms (sklearn,etc) on top of Dask. Not just Numpy/Pandas |
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