In my last post, I showed you tutorial for running Apache Spark on managed kubernetes, Azure Kubernetes Service (AKS).
In this post, I’ll show you the tutorial for running distributed workloads of Dask on AKS.
By using Dask, you can run Scikit-Learn compliant functions and jobs for data which cannot fit in memory, or run in distributed manners. For simplicity, here I’ll use built-in Dask ML function (
dask_ml.linear_model.LinearRegression) in this tutorial. (With the same manners, you can also run regular sklearn functions.)
Cloud managed kubernetes will make you speed up this large ML workloads.
Click through for the process. I’ve had some positive experiences with Dask as a dashboarding tool. It’s definitely one of the better ones if you’re big into Python.