Example - Model Training and Deployment (Iris)

This topic uses Cloudera Machine Learning's built-in Python template project to walk you through an end-to-end example where we use experiments to develop and train a model, and then deploy it using Cloudera Machine Learning.

This example uses the canonical Iris dataset from Fisher and Anderson to build a model that predicts the width of a flower’s petal based on the petal's length.

The scripts for this example are available in the Python template project that ships with Cloudera Machine Learning. First, create a new project from the Python template:

Once you've created the project, go to the project's Files page. The following files are used for the demo:

cdsw-build.sh - A custom build script used for models and experiments. Pip installs our dependencies, primarily the scikit-learn library.
fit.py - A model training example to be run as an experiment. Generates the model.pkl file that contains the fitted parameters of our model.
predict.py - A sample function to be deployed as a model. Uses model.pkl produced by fit.py to make predictions about petal width.