Get started with step-by-step learning paths
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.
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.
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.
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.
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.
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.
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.
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.
