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From Data Insights to Data Products: Demystifying Machine Learning Deployme...

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Webinar Description:

A lot of excitement exists around the potential for non-profit organizations to utilize machine learning to enhance their impact. While countless online resources are available to support data scientists as they build their first models, there is a notable absence of a clear road map for how to deploy models within a production environment. Centered around the experiences of Benefits Data Trust as we have deployed our first machine learning model, this session will provide a framework for data leaders to utilize when deploying models at their organizations.

Attendees will leave this session with a better understanding of the following:
* Opportunities for non-profit organizations to utilize machine learning to enhance impact
* Tips for gaining organizational buy-in - both in terms of aligning ML with organizational culture as well as human centered design considerations
* Technical architecture needed for training models, making predictions, distributing these predictions to front-line users, and evaluating model effectiveness
* Different deployment approaches and considerations for identifying one that best fits your organization
* Resources available to support you in this effort


About the speaker:

Matt Stevens aspires to build tools that harness collective intelligence for the collective good. He currently serves as the Director of Data Science at Benefits Data Trust (BDT) - a national not-for-profit organization based in Philadelphia, PA. In this role, he has the privilege of leading a talented team committed to the use of data, insight, and innovation to transform the way that people in need connect with the benefits and services that improve lives. Matt was recently selected as 2017 Uptake.org data fellow, a 6-month program designed to advance promising data driven leaders within the data for good space by providing professional development and mentorship opportunities. Prior to joining BDT, he contributed to the design and testing of the 2010 Decennial Census as a Mathematical Statistician at the US Census Bureau. Matt holds a BS in Statistics from the University of Tennessee, and an MS in Survey Methodology from the University of Maryland - College Park.

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