The document discusses continuous deployment for machine learning projects using Bodywork. Bodywork enables CI/CD for ML projects by allowing teams to define workflows in a Git repository that can deploy models, pipelines, and services to Kubernetes. It tackles common deployment challenges for ML like managing container images and infrastructure, and allows teams to focus on their code. The talk includes demos of sample ML projects deployed with Bodywork, like serving a model with a REST API and a train-and-serve pipeline.