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.
Tutorial 1: An End-to-End ML Workflow: From Notebook to Kubeflow Pipelines with MiniKF & 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.
Legacy: An end-to-end ML pipeline on-prem: Notebooks & Kubeflow Pipelines on the new MiniKF
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