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Deploying Kubeflow Pipelines with the Kale SDK
Course Number
klsdkSelf-Paced
About This Course
This course focuses on using the Kale SDK to decorate Python code snippets and generate and deploy Kubeflow Pipelines. Throughout this course you will explore, via hands on tutorials and challenges, how to convert Python files into Kubeflow Pipelines. SDK Concepts will be presented and demonstrated in a series of increasingly more advanced sections. By the end of this course, you will be fully prepared to begin deploying your own Kubeflow Pipelines based on Python code.
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 completing the Getting Started with Kubeflow and Deploying Kubeflow Pipelines with the Kale UI courses in advance of taking this course if you are not yet familiar with Kubeflow. Additionally we expect that you are familiar with Python and popular data science Python libraries.