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Hyperparameter Tuning in Kubeflow

In this course, you will learn to hyperparameter tune your models.

  • Course Number

    Course
  • Self-Paced

About This Course

Working with Katib allows you to perform hyperparameter tuning to improve models built with Kale in Jupyter Notebooks. In this course, you will learn to:

  • define Katib experiments using Kubeflow Pipelines,
  • run multiple experiments in parallel, and
  • interpret the results to identify the ideal model.

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 famililar with Kubeflow. Additionally we expect that you are familiar with Python and popular data science Python libraries.

Frequently Asked Questions

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