The text discusses the development and implementation of Datadog's Automatic Faulty Deployment Detection, a feature designed to quickly identify problematic software deployments in services monitored by Datadog Application Performance Monitoring (APM). The process began with an unsupervised learning model due to challenges like label scarcity and data imbalance, as faulty deployments are rare and context-dependent. The team defined faulty deployments by significant increases in error rates and used an iterative framework to refine detection through statistical checks. Later, they transitioned to supervised learning, using weak supervision to improve label quality and model accuracy. This approach allowed for earlier detection of deployment issues, improving both precision and recall. The project highlights how data science and machine learning can enhance software deployment reliability and offers techniques applicable to other domains with similar challenges.