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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.
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.
Update (August 14, 2020): This tutorial has been updated to showcase the Taxi Cab end-to-end example using the new MiniKF (v20200812.0.0) Today, at Kubecon Europe 2019, Arrikto announced the release of the new MiniKF, which features Kubeflow v0.5. The...