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Contributing to Kubeflow Examples

You want to contribute to Kubeflow examples? That's awesome! Please refer to the short guide below.

Contributing Guide

The Kubeflow project is dedicated to making machine learning on Kubernetes simple, portable and scalable. We need your support in making this repo the destination for top models and examples, which show the power of Kubeflow. We have created an initial list of proposed manifests, based on what we think developers need the most guidance. Please feel free to self-assign these examples, by following a simple 3 step process:

  • Identify a manifest in table below and put your github id in the table
  • Create a Github issue with the details of the manifest and self-assign
  • Send a PR to this repo with the actual work for the manifest

We have assigned priorities to the items below. See priority guidance:

  • P0: Very important, try to self-assign if there is a P0 available
  • P1: Important, try to self-assign if there is no P0 available
  • P2: Nice to have

Proposed Manifests

Manifest What does it accomplish? Priority Priority reasoning ML framework Owner (github_id) Company PR link
TensorFlow serving end-to-end How to perform TensorFlow serving on Kubeflow e2e P0 TODO TensorFlow TODO TODO TODO
Zillow housing prediction Zillow's home value prediction on Kaggle P0 High prize Kaggle competition w/ opportunity to show XGBoost XGBoost puneith Google TODO
Mercari price suggestion challenge Automatically suggest product proces to online sellers P0