A repository that showcases how to implement machine learning pipelines on Google cloud platform using Kubeflow. These examples uses the python function-based method of implementing Kubeflow Pipelines.
Note: The jupyter notebook files in the repo explain in detail how to setup the KFP SDK environment and how to connect to the pipelines on Google Cloud Platform
This example illustrates the basic concepts of a Kubeflow pipeline in which the components of the pipeline implements arithmetic operations.
This example illustrates a much more advanced example using the Iris Dataset. In this example we take the Iris dataset solution and implements it into a Kubeflow Pipeline. The Iris Dataset: https://docs.python-guide.org/scenarios/ml/