Red Hat OpenShift Data Science
Red Hat OpenShift Data Science is a part of the Red Hat OpenShift AI portfolio and provides tools across the AI/ML lifecycle.
What's new in OpenShift Data Science?
Red Hat OpenShift Data Science is an AI platform that gives data scientists and developers a powerful open hybrid AI/ML platform for gathering insights from data and building AI-enabled applications. It provides tools to rapidly develop, train, serve, and monitor machine learning models on site, in the public cloud, or at the edge.
When you use OpenShift Data Science here in the developer portal, you are running in a sandbox set up in the service called Red Hat OpenShift Dedicated, which simplifies cloud operations.
Get started with step-by-step learning paths
Learning path
Launch Red Hat OpenShift Data Science
15 minutes | 4 learning resources
In this learning path, we use a pre-existing Jupyter Notebook project to start exploring data science. Project Jupyter offers the interactive JupyterHub and JupyterLab tools, which we'll introduce in this path.
Learning path
Red Hat OpenShift Data Science resources
15 minutes | 2 learning resources
The Red hat OpenShift Data Science platform offers a number of documents and learning materials explaining how to use Red Hat managed services as well as managed services and software offered by our partners. Learning materials include how-to resources, tutorials, and quick starts.
Learning path
How to access, download, and analyze data for S3
20 minutes | 3 learning resources
In this learning path, you will start your Jupyter notebook server and select preferences for S3 usage. You will also learn how to access and download the data you create as well as analyze it, using a variety of skills and tools.
Learning path
How to create a TensorFlow model
20 minutes | 3 learning resources
In this learning path, you will set up options for your Jupyter notebook server, then explore the MNIST dataset to refine your data. Finally, you will learn how to implement frameworks, layers, and nodes to create your TensorFlow model.
Learning path
How to create a PyTorch model
20 minutes | 3 learning resources
In this learning path, you will set up options for your Jupyter notebook server and select your PyTorch preferences, then explore the dataset you'll use to create your model. Finally, you will learn how to build, train, and run your PyTorch model.
Learning path
How to set up and reproduce data science experiments
30 minutes | 5 learning resources
In this learning path, you will learn how to set up data science projects. You will also learn how to consistently reproduce or execute Jupyter notebooks in the data science projects and serve the developed models in the form of a web service on top of Red Hat OpenShift.
Learning path
How to get started with Intel OpenVINO
15 minutes | 2 learning resources
OpenVINO is an open source toolkit to help optimize deep learning performance and deploy using an inference engine onto Intel hardware. Expand your knowledge about Intel, Data Science, and AI Analytics with this learning path.
Learning path
Configure a Jupyter notebook to use GPUs for AI/ML modeling
30 minutes | 3 learning resources
High-performance computing is one of the hottest trends in enterprise tech. In this learning path, you will learn how to prepare your Jupyter notebook server for using a GPU. You will learn how to examine GPU resources and then use these resources to load and run a PyTorch model.
Learning path
Using projects in Red Hat OpenShift Data Science
15 minutes | 2 learning resources
In this learning path, you will create and set up options for your data science project from the Red Hat OpenShift Data Science dashboard. If you can’t remember how to launch OpenShift Data Science, go back to the Launch Red Hat OpenShift Data Science learning path.
Learning path
Install Red Hat OpenShift Data Science in Red Hat OpenShift Service on AWS
30 minutes | 2 learning resources
Red Hat OpenShift Data Science is a platform for data scientists and developers of artificial intelligence (AI) applications. It provides a fully supported environment that lets you rapidly develop, train, test, and deploy machine learning models on-premises and/or in the public cloud.
Learning path
Model serving in RHODS
15 minutes | 3 learning resources
In this learning path, you will create a model server and deploy a model in your data science project from the Red Hat OpenShift Data Science dashboard. If you can’t remember how to launch OpenShift Data Science, go back to the Launch Red Hat OpenShift Data Science learning path.
Red Hat OpenShift Data Science learning path
Using projects in Red Hat OpenShift Data Science
Red Hat OpenShift Data Science is a platform for data scientists and developers of artificial intelligence (AI) applications. It provides a fully supported environment that lets you rapidly develop, train, test, and deploy machine learning models on-premises and/or in the public cloud. OpenShift Data Science is provided as a managed cloud service add-on to the OpenShift cloud services or as self-managed software that you can install on-premise or in the public cloud on OpenShift.
In this learning path, you will create and set up options for your data science project from the Red Hat OpenShift Data Science dashboard. If you can’t remember how to launch OpenShift Data Science, go back to the Launch Red Hat OpenShift Data Science learning path.
