End-to-end ML pipeline architecture on AWS using RapidCanvas for data ingestion, transformation, model training, and serving predictions. CSV files land in S3, flow through RapidCanvas S3 Connector into Recipe ETL pipelines that load MySQL Database, which feeds both Backend API REST services and ML Recipe Training workflows generating ML Projections. React-based DataApps Frontend consumes Backend API to deliver predictions to Users, demonstrating a complete data-to-insight workflow. This architecture solves the challenge of orchestrating ETL and ML training in a unified platform while maintaining separation between data storage, model training, and application layers. Fork this diagram on Diagrams.so to customize connectors, swap MySQL for Redshift or Snowflake, or add real-time streaming components.