Tutorial 2: Hyperparameter tuning with Kubeflow and Kale

Tutorial 2: Hyperparameter tuning with Kubeflow and Kale

In this tutorial, we will use Kale to unify the workflow across the above components, and present a seamless process to create ML pipelines for HP tuning, starting from your Jupyter Notebook. We will use Kale to convert a Jupyter Notebook to a Kubeflow Pipeline without any modification to the original Python code. Pipeline definition and deployment is achieved via an intuitive GUI, provided by Kale’s JupyterLab extension.

Tutorial 1: Deploy Kubeflow Pipelines with Kale

Tutorial 1: Deploy Kubeflow Pipelines with Kale

Kubeflow is the de facto standard for running Machine Learning workflows on Kubernetes. Jupyter Notebook is a very popular tool that data scientists use every day to write their ML code, experiment, and visualize the results. However, when it comes to converting a Notebook to a Kubeflow Pipeline, data scientists struggle a lot.