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Course Number
CourseSelf-Paced
About This Course
When deploying Kubeflow production environments, you will want to snapshot your notebooks, pipelines, and volumes so that you can rapidly restore environments or specific Kubeflow Pipeline steps. In this course, you will learn:
- how Rok takes snapshots,
- how to restore snapshots, and
- how to manage the volumes in the Kubernetes Pods.
Course Certificate
This course has an accompanying certificate which you can trigger and access in the final sections of the course. This certificate is an HTML-based certificate and can be shared on any medium of your choosing.
Kubeflow as a Service Usage
Because it’s the simplest way to get started, we will use Kubeflow as a Service as our Kubeflow environment. We’ll teach you how to organize and annotate cells in a Jupyter Notebook to define a Kubeflow Pipeline that will run on a Kubernetes cluster. This does not require any specialized knowledge of Kubernetes. Instead, we’ll use the open-source Kale JupyterLab extension.
Requirements
We strongly recommend you know how to organize and annotate cells in a Jupyter Notebook to define a Kubeflow Pipeline that will run on a Kubernetes Cluster. If you need a refresher on these skills, please review the Deploying Kubeflow Pipelines with the Kale UI course. We are also assuming that you know how to use Katib to perform hyperparameter tuning with Kubeflow Pipelines. If you need a refresher on these skills please review the Hyperparameter Tuning in Kubeflow course. Lastly, we assume familiarity with Python and popular packages and libraries used by Data Scientists.