0 / 0
Training and deploying machine learning models in notebooks

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.

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

Next steps

Parent topic: Managing predictive deployments

Generative AI search and answer
These answers are generated by a large language model in watsonx.ai based on content from the product documentation. Learn more