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Tutoria 4: AutoML with Kubeflow and Kale
Tutorial 3: Model Serving 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
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
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...