Online / 5 & 6 February 2022


Making Apache Spark, Apache Mahout, Kubeflow, and Kubernetes Play Nice

Working with big data matrices is challenging, Kubernetes allows users to elastically scale, but can only have a pod as large as a node, which may not be large enough to fit the matrix in memory. While Kubernetes allows for other paradigms on top of it which allows pods to coordinate on individual jobs, setting them up and making them play nice with ML platforms is not straightforward. Using Apache Spark and Apache Mahout we can work with matrices of any dimension and distribute them across an unbounded number of pods/nodes, and we can use Kubeflow to make our work quickly and easily reproducible. In this talk, we’ll discuss how we used Apache Spark and Mahout to denoise DICOM images of lungs of COVID patients and published our Pipeline with Kubeflow to make the process easily repeatable which could help doctors in more resource limited hospitals, as well as other researchers seeking to automate the detection of COVID.


Photo of Trevor Grant Trevor Grant