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Deploy Kubeflow Pipelines with Kale
This course will guide you through a seamless workflow that enables data scientists to deploy a Jupyter Notebook as a Kubeflow pipeline with the click of a button. Moreover, we will showcase how a data scientist can reproduce a step of the pipeline run, debug it, and then re-run the pipeline without having to write a single line of code.
Course Number
CourseSelf-Paced
About This Course
In this course, we will focus on two essential aspects:
- Low barrier to entry: convert a Jupyter notebook to a multi-step Kubeflow pipeline in the Cloud using only the GUI.
- Reproducibility: automatic data versioning to enable reproducibility and better collaboration between data scientists.
This course was presented as a workshop by Google & Arrikto during KubeCon San Diego 2019. Here are the slides, and the video of the workshop.
You will build a complex, multi-step ML pipeline with Kubeflow pipelines, without using any CLI commands or SDKs. You also won’t need to write any code for pipeline components and Pipelines DSL, or build any Docker images.