Explore a 23-minute conference talk on deploying machine learning models for forensic anthropology using Docker and Streamlit. Discover how an archaeologist and physicist created a web application for skeletal sex prediction without prior web development experience. Learn about the use of pandas and scikit-learn for data analysis and model construction, and how Streamlit simplified the web application design process. Gain insights into packaging code and dependencies into a Docker image, and deploying it as a container on a virtual machine. Follow the step-by-step process of building and deploying the SexEst web application, including Dockerfile creation, Docker installation on a web server, and container deployment. Understand the implications of this project for forensic anthropology and bioarchaeology, and explore references to Streamlit, the SexEst application, and its GitHub repository.