Training and deploying machine learning models in notebooks

This topic contains a brief introduction to Jupyter notebooks and helpful links to example notebooks.

If you choose to build a machine learning model in a notebook, you should be comfortable with coding in Jupyter notebook. A Jupyter notebook is a web-based environment for interactive computing. You can run small pieces of code that process your data, and then immediately view the results of your computation. Using this tool, you can assemble, test, and run all of the building blocks you need to work with data, save the data to the Watson Machine Learning, and deploy the model.

• For details on using notebook editors, see Notebooks.
• For details on working with notebooks, see Coding and running notebooks.
• For details on authenticating in a notebook, see Authentication.

Learn from sample notebooks

Since there are many ways to build and train models and then deploy them, the best way to learn is to look at annotated samples that step you through the process using different frameworks. For details, see:

• Machine Learning Python client samples and examples
• Machine Learning REST API samples and examples

Next steps

• Learn how to deploy a trained model from the deployment space.