Notebooks have traditionally been a tool for drafting code and avoiding repeated expensive computations while exploring solutions. The Machine Learning space has used these extensive as a place to prototype. However, with new tools like nteract's papermill library, this technology has been expanded to make a reusable and parameterizable template for execution.
We'll walk though what Jupyter notebooks are and how they are being programmatically used at Netflix. We’ve had large adoption of notebooks for specific use-cases and can show how this helps with our batch processing world. Specifically we'll explore how notebooks make a great reproducible log of execution and act as a sharable medium for useful integration patterns.
Matthew Seal is a senior software engineer at Netflix, where he works on scaling data platform solutions. Based in the Bay Area of California, Matthew attended Stanford University for undergraduate and graduate school. He stayed in the area, working at startups and spending a long stretch of time working at OpenGov.
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