Deploying Machine Learning Models for Forensic Anthropology with Docker and Streamlit

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.

Intro
What is machine learning?
The problem
How to use the models
Why Streamlit
What is Docker?
Docker images - Docker Hub
Docker images - Dockerfile
Docker images - requirements.txt
Docker images - final result