Tutorial 3: Build an ML pipeline with hyperparameter tuning and serve the model starting from a notebook
Tutorial 2: 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.
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.