Creating a Kubeflow pipeline to build, train, and deploy a sample ML model

In this section, we will be using the Fashion MNIST dataset and TensorFlow’s Basic classification to build the pipeline step by step and turn the example ML model into a Kubeflow pipeline.

Before deploying Kubeflow, we will look at the dataset that we are going to use. Fashion-MNIST (https://github.com/zalandoresearch/fashion-mnist) is a Zalando article image dataset that includes a training set of 60,000 samples and a test set of 10,000 examples. Each sample is a 28 x 28 grayscale image with a label from one of 10 categories.

Each training or test item in the dataset is assigned to one of the following labels:

Table 9.1 – Categories in the Fashion MNIST dataset

Now that our dataset is ready, we can launch a new notebook server via the Kubeflow dashboard.

Step 1 – launching a new notebook server from the Kubeflow dashboard

You can start...