The Rise (and Lessons Learned) of ML Models to Personalize Content on Home (Part I)

This post details the evolution of Spotify's Home page recommendation ML models, specifically addressing the challenges of unifying training and serving data. It introduces the concept of a single transformation implementation for features to prevent discrepancies between training and serving data, exemplified by the Shortcuts Model migration. It also highlights the adoption of Tensorflow Data Validation (TFDV) for daily comparison of training and serving data schemas and feature distributions, with Chebyshev distance used for alerting on drift. The post emphasizes the shift from batch predictions to real-time serving and the automation of feature logging, data validation, and model retraining within Kubeflow pipelines.