In this repo you will explore step-by-step guide to deploy Jupyter Notebook on OpenShift and run a Machine Learning model.
OpenShift - an application management platform Containers are seen a foundational technology to building modern apps that are portable, secure and scale - and the industry standard for container management and orchestration is Kubernetes. OpenShift is the industry-leading Kubernetes based container platform that provides a self-service seamless experience for Data Scientists to easily deploy and use the Machine Learning tools, empowering them to create and share the modeling results in a consistent way with peers and software developers without dependency.
Red Hat provides the industry's leading hybrid cloud platform, Red Hat OpenShift. Built on a foundation of Linux, Kubernetes, and open-source technologies, it enables modern, hybrid-cloud application development and lifecycle management for any application, on any infrastructure, in any location. OpenShift is tried, tested, and trusted to deliver mission-critical innovation everywhere.
About Jupyter Notebook The Jupyter Notebook is an open source web application that you can use to create and share documents that contain live code, any mathematical or statistical equations, visualizations, and text. Jupyter Notebook is well-suited for demonstrations of programming concepts and training. In a single window, we enter the inputs and generate outputs. It is a fundamental way of telling stories. Stories that describe the insights into the relevant data. Jupyter Notebook is maintained by the people at Project Jupyter.
Reference:
OpenShift, being a Kubernetes distribution, you can use the JupyterHub deployment method for Kubernetes created by the Jupyter project team
https://github.com/jupyter-on-openshift/jupyter-notebooks#jupyter-notebooks-for-openshift