We spend a lot of time focused on giving data scientists the best experience for deploying their machine learning models. We think they should not only use the best tools for the job, they should also be able to integrate their work easily with other tools. Today we’ll highlight one such integration: Jupyter Notebook.
When we work in Jupyter Notebook—an open-source project tool used for creating and sharing documents that contain live code snippets, visualizations, and markdown text—we are reminded how easy it is to use our API to deploy a machine learning model from within a notebook.
About Jupyter Notebook
These notebooks are popular among data scientists and are used both internally and externally to share information about data exploration, model training, and model deployment. Jupyter Notebook supports running both Python and R, two of the most common languages used by data scientists today.
How We Use It
We don’t think you should be limited to creating algorithms/models solely through our UI. Instead, to give data scientists a better and more comprehensive experience, we built an API endpoint that gives more control over your algorithms. You can create, update, and publish algorithms using our Python client, which lets data scientists deploy their models directly from their existing Jupyter Notebook workflows.
We put together the following walkthrough to help guide users through the process to deploy from within a notebook. The first few steps are shown below:
In this example, after setting up credentials, we download and prep a dataset and build and deploy a text classifier model. You can see the full example notebook here. And for more information about our API, please visit our guide.
More of a Visual Learner?
Watch this short demo that goes through the full workflow.