Using Jupyter Notebooks in OpenShift
Jupyter Notebooks is the de facto standard environment for data scientists and data engineers to analyze data and build ML models. Since the notebooks provided by the platform run as containers, you will see how your team can start quickly and consistently by adopting the platform. The platform provides a rapid way to develop, train, and test ML models and deploy them onto production. In the ODS platform, the Jupyter Notebooks environments are referred to as workbenches. You will learn about workbenches later in this section. But first, we need to learn how to create these environments.
We’ll start by provisioning S3 object storage for you to access the data required for the model training process. This is part of the platform setup, and data scientists will not have to execute these steps for their day-to-day work.