Kubeflow – Tutorials, Samples, and Shared Resources

Docs: Kubeflow Samples

Mon, 01 Jan 0001 00:00:00 +0000

This section introduces the examples in the kubeflow/examples repository. Before using a sample, check the sample’s README file for known issues.

MNIST image classification

Last update 2023/10/19 Kubeflow v1.0.0

Train and serve an image classification model using the MNIST dataset. This tutorial takes the form of a Jupyter notebook running in your Kubeflow cluster. You can choose to deploy Kubeflow and train the model on various clouds, including Amazon Web Services (AWS), Google Cloud Platform (GCP), IBM Cloud, Microsoft Azure, and on-premises. Serve the model with TensorFlow Serving.

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Financial time series

Last update 2022/02/10 Kubeflow v0.7

Train and serve a model for financial time series analysis using TensorFlow on Google Cloud Platform (GCP). Use the Kubeflow Pipelines SDK to automate the workflow.

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Next steps

Work through one of the Kubeflow Pipelines samples.

Docs: Codelabs, Workshops, and Tutorials

Mon, 01 Jan 0001 00:00:00 +0000

Agile Stacks tutorials for Kubeflow Pipelines

Run Kubeflow Pipelines tutorials on AWS, GCP, or on-prem hardware using Agile Stacks. Pipeline templates provide step-by-step examples for working with object storage filesystem, Kaniko, Keras, and Seldon.

ML Pipeline Templates: End-to-end Tutorial.

Google codelabs

Google Developers Codelabs provide a guided, tutorial, hands-on coding experience.

Introduction to Kubeflow on GKE: Run MNIST with Kubeflow on Google Kubernetes Engine (GKE).
Kubeflow Pipelines - GitHub Issue Summarization: Run GitHub Issue Summarization with Kubeflow Pipelines on GKE.
From Notebook to Kubeflow Pipelines with MiniKF and Kale: Run an end-to-end ML workflow all the way from a Notebook to a reproducible multi-step pipeline with Kale on MiniKF.

Docs: Blog Posts

Mon, 01 Jan 0001 00:00:00 +0000

The following blog posts provide detailed examples and use cases. Be aware that a blog post describes the interfaces at the time of publication of the post. Some interfaces are under rapid development and therefore may change frequently.

The Kubeflow blog

Visit the Kubeflow blog to keep up to date with news about the project and to learn how to use the latest features.

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Orchestrating ML pipelines at scale with Kubeflow Pipelines

February 3, 2020

This article explains how to use Kubeflow Pipelines to overcome long ML training jobs, manual experimentation, reproducibility, and DevOps obstacles.

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Using Kubeflow to train and serve a PyTorch model in Google Cloud Platform

January 23, 2019

This example demonstrates how you can use Kubeflow to train and serve a distributed Machine Learning model with PyTorch on a Google Kubernetes Engine cluster in Google Cloud Platform (GCP).

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Getting started with Kubeflow Pipelines

November 16, 2018

This article describes how you can tackle ML workflow operations with Kubeflow Pipelines, and highlights some examples that you can try yourself. The examples revolve around a TensorFlow ‘taxi fare tip prediction’ model, with data pulled from a public BigQuery dataset of Chicago taxi trips.

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How to create and deploy a Kubeflow machine learning pipeline

November 22 - December 4, 2018

A series of articles that walk you through the process of taking an existing real-world TensorFlow model and operationalizing the training, evaluation, deployment, and retraining of that model using Kubeflow Pipelines. [Part 1](https://towardsdatascience.com/how-to-create-and-deploy-a-kubeflow-machine-learning-pipeline-part-1-efea7a4b650f) (creating and deploying a pipeline), and [part 2](https://towardsdatascience.com/how-to-deploy-jupyter-notebooks-as-components-of-a-kubeflow-ml-pipeline-part-2-b1df77f4e5b3) (using Jupyter notebooks).

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Docs: Videos

Mon, 01 Jan 0001 00:00:00 +0000

The following tutorials and overviews published in video format. Be aware that a video describes the interfaces at the time of publication of the video. Some interfaces are under rapid development and therefore may change frequently.

Keynote: Machine Learning using Kubeflow and Kubernetes

February 26, 2020

Presenter: Arun Gupta.

Kubeflow – Tutorials, Samples, and Shared Resources

Docs: Kubeflow Samples

MNIST image classification

Financial time series

Next steps

Docs: Codelabs, Workshops, and Tutorials

Agile Stacks tutorials for Kubeflow Pipelines

Google codelabs

Docs: Blog Posts

The Kubeflow blog

Orchestrating ML pipelines at scale with Kubeflow Pipelines

Using Kubeflow to train and serve a PyTorch model in Google Cloud Platform

Getting started with Kubeflow Pipelines

How to create and deploy a Kubeflow machine learning pipeline

Docs: Videos

Keynote: Machine Learning using Kubeflow and Kubernetes

Enabling Kubeflow with Enterprise-Grade Auth for On-Prem Deployments

Supercharge Kubeflow Performance on GPU Clusters

Towards Continuous Computer Vision Model Improvement with Kubeflow

Measuring and Optimizing Kubeflow Clusters at Lyft

Tutorial: From Notebook to Kubeflow Pipelines: An End-to-End Data Science Workflow

Building a Medical AI with Kubernetes and Kubeflow

Panel: Enterprise-grade, On-prem Kubeflow in the Financial Sector

Advanced Model Inferencing Leveraging KNative, Istio & Kubeflow Serving

Building and Managing a Centralized Kubeflow Platform at Spotify

Hyperparameter Tuning Using Kubeflow

Managing Machine Learning in Production with Kubeflow and DevOps

Machine Learning as Code: and Kubernetes with Kubeflow

Artificial Intelligence at Cisco with Kubeflow

CNCF (Cloud Native Computing Foundation) channel

Google Cloud Platform channel

Docs: Shared Resources and Components

Check the provider of any resource that you use

AI Hub

Reusable components for Kubeflow Pipelines