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Abstract: Visual representations of data inform how machine learning practitioners think, understand, and decide. Before charts are ever used for outward communication about a ML system, they are used by the system designers and operators themselves as a tool to make better modeling choices. Practitioners use visualization, from very familiar statistical graphics to creative and less standard plots, at the points of most important human decisions when validation of those decisions can be difficult. Visualization approaches are used to understand both the data that serves as input for machine learning and the models that practitioners create. In this talk, learn about the process of building a ML model in the real world, how and when practitioners use visualization to make more effective choices, and considerations for ML visualization tooling.
Bio: Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source modeling tools. She is both an international keynote speaker and a real-world practitioner focusing on data analysis and machine learning practice. She is the author of Text Mining with R with her coauthor David Robinson and Supervised Machine Learning for Text Analysis in R with her coauthor Emil Hvitfeldt. She loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.