Abstract:- In this talk, we propose a generalized machine learning framework for e-commerce businesses. The framework is responsible for over 30 different user-level predictions including lifetime value, recommendations, churn predictions, engagement and lead scoring. These predictions provide a vital layer of intelligence for a digital marketer. Kinesis is used to capture browsing information from over 120M users across 100 companies (both in-app and web). A data processing and feature engineering layer is build on Apache Spark. These features provide inputs to predictive models for business applications. Different models each for Churn, Lifetime value, Product recommendation and search are written on Spark. These models can be plugged into any marketing campaign for any integrated e-commerce company leading to a generalized system. We finally present a monitoring system for machine learning called RS Sauron. This system provides more than 200 objective metrics measuring the health of predictive models, and depicts KPIs for model accuracy in a continual setting.