2 years ago

In the last part of this series, we created the shared PVCs to enable collaboration among data scientists, machine learning engineers, and the DevOps team. Next, we will leverage the storage volumes and container images to build a simple machine learning pipeline based on three independent Notebook Servers.

The current installment of this tutorial series focuses on building a Notebook Server for the data scientists to convert a set of images into a dataset ready to be used by the ML engineers to build and train a model.

These PVCs will be mounted in the Notebook Server pods to write shared artifacts such as the dataset and models.

Give a name of your choice to the Notebook Server and choose the custom image option to provide the name of the Docker image built for data preparation.