Blocking Slack Invite Spam With Machine Learning

This post details the implementation of a machine learning model to block invite spam. It describes the transition from a labor-intensive, hand-tuned rule-based system to a supervised ML problem. The approach uses invite acceptance rate as a proxy label and trains a sparse logistic regression model on approximately 60 million features, including user/team IDs, email addresses, domains, IP addresses, text stems, character sequences, websites, and team age. The post also covers model serving via an in-house Kubernetes-based microservice and highlights the impact of reduced false positives and automated human effort.