Train the Model
This topic shows you how to run experiments and develop a model
fit.py script tracks metrics, mean squared error
(MSE) and R2, to help compare the results of different
experiments. It also writes the fitted model to a
- Navigate to the Iris project's page.
- Click Run Experiment.
- Fill out the form as follows and click Start
Run. Make sure you use the Python 3 kernel.
- The new experiment should now show up on the Experiments table. Click on the Run ID to go to the experiment's Overview page. The Build and Session tabs display realtime progress as the experiment builds and executes.
- Once the experiment has completed successfully, go back to
its Overview page. The tracked metrics show us
that our test set had an MSE of ~0.0078 and an R2 of
~0.0493. For the purpose of this demo, let's consider this an accurate
enough model to deploy and use for predictions.
- Once you have finished training and comparing metrics from
different experiments, go to the experiment that generated the best
model. From the experiment's Overview page,
model.pklfile and click Add to Project.This saves the model to the project filesystem, available on the project's Files page. We will now deploy this model as a REST API that can serve predictions.