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: Sven Degroote

This example covers the following concepts:

1. Deploying Kubeflow to a GKE cluster
2. Exploration via JupyterHub (prospect data, preprocess data, develop ML model)
3. Training several tensorflow models at scale with TF-jobs
4. Deploy and serve with TF-serving
5. Iterate training and serving
6. Training on GPU

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

• Slack Channel: #kubeflow-examples
• Twitter
• Mailing List

In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.

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