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