Event Pipeline to Production RAG Platform

Scenario

A platform team uses Terraform to deploy an event-driven data ingestion pipeline (EventBridge + DataWorks + RDS/ESS) that feeds training data into PAI, where custom embedding and reranking models are trained, then deploys those models into a production RAG system (Bailian neural reranking over OpenSearch/Elasticsearch) all managed as unified IaC — creating a complete loop from raw event data through model training to production intelligent search.

How the products combine

1. dataworks · event-driven-ml-feature-pipeline-platform-61b6e5 — Event-Driven ML Feature Pipeline Platform

See _combos/event-driven-ml-feature-pipeline-platform-61b6e5.

2. alinux · compliant-infra-with-ml-search-and-identity-148b67 — Compliant Infra with ML Search and Identity

See _combos/compliant-infra-with-ml-search-and-identity-148b67.

3. airec · production-rag-platform-with-neural-reranking-an-6e7440 — Production RAG Platform with Neural Reranking and Infrastructure-as-Code

See _combos/production-rag-platform-with-neural-reranking-an-6e7440.

4. airec · custom-model-enhanced-rag-recommendation-platfor-ec855c — Custom Model-Enhanced RAG Recommendation Platform

See _combos/custom-model-enhanced-rag-recommendation-platfor-ec855c.

Typical questions

• build event pipeline that trains models and deploys RAG
• EventBridge data ingestion to PAI training to production RAG
• terraform deploy full ML pipeline from data to search
• event-driven feature engineering feeding RAG chatbot
• end-to-end ML platform from ingestion to neural reranking
• 从事件数据到模型训练到生产级RAG的完整平台
• Terraform部署事件管道加PAI训练加RAG搜索
• DataWorks数据加工到Bailian智能检索全链路

Q: How do you build an end-to-end event-driven ML pipeline that trains models and deploys a production RAG system? A: The platform uses Terraform to deploy an event-driven data ingestion pipeline that feeds training data into PAI for model training before deploying the results into a production RAG system. Managed as unified infrastructure-as-code, this architecture creates a complete loop from raw event data through custom embedding and reranking model training to production intelligent search.