+++
title = "Blog Posts"
description = "Blog posts and articles about Kubeflow"
weight = 30
+++

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.

{{% blocks/content-item title="The Kubeflow blog"
  url="https://medium.com/kubeflow" %}}
Visit the Kubeflow blog to keep up to date with news about the project and to
learn how to use the latest features.
{{% /blocks/content-item %}}

{{% blocks/content-item title="Using Kubeflow to train and serve a PyTorch model in Google Cloud Platform"
  date="January 23, 2019"
  url="https://medium.com/kubeflow/end-to-end-kubeflow-tutorial-using-a-pytorch-model-in-google-cloud-platform-10fef557a089" %}}
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).
{{% /blocks/content-item %}}


{{% blocks/content-item title="Getting started with Kubeflow Pipelines"
  date="November 16, 2018"
  url="https://cloud.google.com/blog/products/ai-machine-learning/getting-started-kubeflow-pipelines" %}}
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.
{{% /blocks/content-item %}}

{{% blocks/content-item title="How to create and deploy a Kubeflow machine learning pipeline"
  date="November 22 - December 4, 2018"
  url="https://towardsdatascience.com/how-to-create-and-deploy-a-kubeflow-machine-learning-pipeline-part-1-efea7a4b650f" %}}
 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).
{{% /blocks/content-item %}}
