While a prototype may not be production standard, it’s an effective technique companies use to provide stakeholders with insight into a proposed solution. A better solution is to convert your project into a prototype with a frontend that can be deployed on internal servers. Others will take the initiative to convert their notebooks to scripts for somewhat production-grade code.īoth of these end points restrict a project’s accessibility, requiring knowledge of source code hosting sites like GitHub and Bitbucket. What happens next? For many, the project ends there, with their models sitting isolated in a Jupyter notebook. ![]() Imagine that you’re working on a machine learning (ML) project and you’ve found your champion model.
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