Training and deploying machine learning models in notebooks
If you choose to build a machine learning model in a notebook, you must 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 Watson Machine Learning, and deploy the model.
- For details on using notebook editors, refer to Notebooks.
- For details on working with notebooks, refer to Coding and running notebooks.
- For details on authenticating in a notebook, refer to Authentication.
Learn from sample notebooks
Many ways exist to build and train models and then deploy them. Therefore, the best way to learn is to look at annotated samples that step you through the process by using different frameworks. For details, refer to:
- Watson Machine Learning Python client samples and examples
- Watson Machine Learning REST API samples and examples
- Learn how to deploy a trained model from the deployment space.
Parent topic: Deploying and managing models