Red Hat OpenShift Data Science learning

Get started with step-by-step learning paths

  Learning path
Launch RHODS icon

Launch Red Hat OpenShift Data Science 

15 minutes | 4 learning resources

In this learning path, we use a pre-existing Jupyter Notebookproject to start exploring data science. Project Jupyter offers the interactive JupyterHubJupyterLab tools, which we'll introduce in this path.

 Learning path
RHODS resources icon

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
S3 data icon

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
Tensorflow image

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
Pytorch graphic

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
RHODS experiments icon

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
Launch RHODS icon

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
Launch RHODS icon

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.

Developer sandbox use-case activity

Master NLP using Red Hat OpenShift Data Science

In this tutorial, you are an intern for a city transportation department.  You have been given the job to process potential bus repair issues that the drivers have noticed during their shifts. In order to keep the repair issues organized and visible, you will need to learn how to categorize them.

And all of this without having to install anything on your computer, thanks to Red Hat OpenShift Data Science!

Start the exercise

Sample app activity