Commoditizing Music Machine Learning : Services

This post details the commoditization of music machine learning services by building a similarity infrastructure and a service-oriented architecture to manipulate vectors and build recommendations as a last-mile effort. It describes the development of an orchestration process ('doorman') to ensure atomic updates of musical vectors across the system, separating user vector generation from training and enabling real-time reactions to user behavior. The post also mentions the migration of infrastructure to Google Cloud and the challenges of building a consistent feedback framework across distributed models.