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Best Practices in DS on Rapid Model Development & Faster Production Deployment

A key step in the data science workflow is rapid model development in order create, test, and identify the best models to put into production. However, large gaps exist in this workflow, and the data science tool set is rapidly changing to fill those gaps. Large teams and enterprises are quickly moving from using individual siloed notebooks like Zeppelin and Jupyter to wanting to share and reuse models, code and results. Challenges also exist in deploying models into production and model serving using tools like Kubeflow and Tensorflow. We will discuss real-world examples of how companies are solving these problems, and how you can use these best practices in your own workflow.

Speaker: Moonsoo Lee, Creator of Apache Zeppelin & Co-Founder/CTO, Zepl

Monday, 07/22/19


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