A repository to share extended Kubeflow examples and tutorials to demonstrate machine learning concepts, data science workflows, and Kubeflow deployments. They illustrate the happy path, acting as a starting point for new users and a reference guide for experienced users.

This repository is home to three types of examples:

1. End-to-end
2. Component-focused
3. Application-specific

Author: Hamel Husain

This example covers the following concepts:

1. Natural Language Processing (NLP) with Keras and Tensorflow
2. Connecting to Jupyterhub
3. Shared persistent storage
4. Training a Tensorflow model
5. CPU
6. GPU
7. Serving with Seldon Core
8. Flask front-end

Author: Elson Rodriguez

This example covers the following concepts:

1. Image recognition of handwritten digits
2. S3 storage
3. Training automation with Argo
4. Monitoring with Argo UI and Tensorboard
5. Serving with Tensorflow

Author: Daniel Castellanos

This example covers the following concepts:

1. Gathering and preparing the data for model training using K8s jobs
2. Using Kubeflow tf-job and tf-operator to launch a distributed object training job
3. Serving the model through Kubeflow's tf-serving

Author: Puneith Kaul

This example covers the following concepts:

1. Training an XGBoost model
2. Shared persistent storage
3. GCS and GKE
4. Serving with Seldon Core

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The Kubeflow community is guided by our Code of Conduct, which we encourage everybody to read before participating.