Remove workflow section (kubeflow#372)

content/docs/about/kubeflow.md

@@ -49,20 +49,6 @@ You can choose to deploy your workloads locally or to a cloud environment.
 
 Kubeflow started as an open sourcing of the way Google ran [TensorFlow](https://www.tensorflow.org/) internally, based on a pipeline called [TensorFlow Extended](https://www.tensorflow.org/tfx/). It began as just a simpler way to run TensorFlow jobs on Kubernetes, but has since expanded to be a multi-architecture, multi-cloud framework for running entire machine learning pipelines.
 
-## Workflow 
-
-The basic workflow is:
-
-* Download the Kubeflow scripts and configuration files.
-* Customize the configuration.
-* Run the scripts to deploy your containers to your chosen environment.
-
-You adapt the configuration to choose the platforms and services that you want
-to use for each stage of the ML workflow: data preparation, model training, 
-prediction serving, and service management.
-
-You can choose to deploy your workloads locally or to a cloud environment.
-
 ## Notebooks
 
 Included in Kubeflow is [JupyterHub](https://jupyterhub.readthedocs.io/en/stable/) to create and manage multi-user interactive Jupyter notebooks. Project Jupyter is a non-profit, open-source project to support interactive data science and scientific computing across all programming languages.