PyData October Presentation Night

Hello PyDataneers,

Let's meet on a fall evening to mingle, network, and hear an awesome talk on how to implement machine learning models in real world applications.

Our honored guest will be Andrew Therriault.

Schedule:

18:00 - 18:30 Gathering and mingling

18:35 - 20:00 Presentation, Q&A, some more mingling

Learning in Cycles: Implementing Sustainable Machine Learning Models in Production

Andrew Therriault

Machine learning textbooks tend to focus too narrowly on specific algorithms or code without looking at the bigger picture. One key real-world application that's rarely covered: predictive models which are regularly updated with new data stemming from earlier predictions. Done poorly, repeated models can amplify the errors and biases of their initial versions. But when done right, they can learn from those mistakes over time, and employ the results of previous versions as new training data to keep the model fresh and productive over the course of months or years of applied use. With examples from my own work in the political, nonprofit, and civic data science fields, this talk will introduce a framework for designing machine learning models that get better over time.

About the speaker:

Andrew Therriault joined the City of Boston as its first Chief Data Officer in 2016, after serving as Director of Data Science for the Democratic National Committee. He received his PhD in political science from NYU in 2011 and completed a postdoctoral research fellowship at Vanderbilt, and more recently served as editor of "Data and Democracy: How Political Data Science is Shaping the 2016 Elections" (O'Reilly Media). Therriault leads Boston’s Analytics Team, a group that is a nationally-recognized leader in using data science to improve city operations and make progress in critical areas such as public safety, transportation, citizen engagement, and public health.