Tutorials

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Build An End-to-End ML Workflow: From Notebook to HP Tuning to Kubeflow Pipelines with Kale

Build An End-to-End ML Workflow: From Notebook to HP Tuning to Kubeflow Pipelines with 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.