Machine Learning with Kubeflow on Amazon EKS with Amazon EFS

Training Machine Learning models involves multiple steps, it gets more complex and time consuming when the size of the data set for training is in the range of 100s of GBs. Data Scientists run through large number of experiments and research which includes testing and training large number of models. Kubeflow provides various ML capabilities to accelerate the training process and run simple, portable scalable Machine Learning workloads on Kubernetes. Model parallelism is a distributed training method in which the deep learning model is partitioned across multiple devices, within or across instances. When Data Scientists adopt Model parallelism there's also a need to share the large dataset across Machine Learning models.