2022-07-27

Good practices for applied machine learning - from model development to model deployment

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Speakers

portrait of Julia Silge in front of gray and tan textured background

Julia Silge

Data Scientist & Software Engineer at Posit, PBC
Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source tools for machine learning and MLOps. She holds a PhD in astrophysics and has worked as a data scientist in tech and the nonprofit sector, as well as a technical advisory committee member for the US Bureau of Labor Statistics. She is a coauthor of Tidy Text Mining with R, Supervised Machine Learning for Text Analysis in R, and Tidy Modeling with R. An international keynote speaker and a real-world practitioner focusing on data analysis and machine learning, Julia loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.

Max Kuhn

Principal Software Engineer (tidymodels) at Posit PBC
Max Kuhn works at Posit PBC, developing software for data analysis and modeling. He previously worked in pharmaceutical and molecular diagnostic research for more than 18 years. Max’s interests are in predictive modeling and machine learning, and he is the author of numerous R packages. He and Kjell Johnson published the bestselling book Applied Predictive Modeling in 2013. Max holds a B.S. in Mathematics and a Ph.D. in Biostatistics.