Training any Deep Learning model on a large dataset often takes a lot of time, especially when the size of the data set for training is in the range of 100s of GBs. And running such machine learning model at scale on cloud demands a sophisticated mechanism.
In this webinar, Suman Debnath, Principal Developer Advocate at AWS, discussed how to use the open-source machine learning toolkit Kubeflow. Suman demonstrated how to deploy Kubernetes cluster utilizing Amazon Elastic Kubernetes Service (EKS) and Amazon Elastic File System (EFS) as persistent storage in the backend, which is utilized for staging the dataset for training, hosting jupyter notebooks, and running the machine learning model.
– Why machine learning with containers
– Kubeflow and Kubeflow Pipelines
👉To try the demo yourself here
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