End-to-End AI Recommendation Pipeline

Scenario

Deploy a custom inference model on Alibaba Cloud Linux, deploy an embedding model in OpenSearch for vector-based candidate retrieval, and deploy AIRec as the recommendation orchestration and serving layer — forming a complete recall-to-ranking recommendation system.

How the products combine

1. alinux · alinux-deploy-model — Alibaba Cloud Linux — Deploy AI models for inference or training

See alinux/alinux-deploy-model.

2. opensearch · opensearch-deploy-model — OpenSearch — Deploy embedding model for inference

See opensearch/opensearch-deploy-model.

3. airec · airec-deploy-service — AIRec — Deploy AIRec service

See airec/airec-deploy-service.

Typical questions

• build recommendation system
• deploy recommendation pipeline
• end-to-end recommendation
• AI推荐系统搭建
• 部署推荐服务
• embedding retrieval plus ranking
• 推荐系统端到端部署
• vector search recommendation

Q: How do I build and deploy an end-to-end AI recommendation system? A: You build and deploy an end-to-end AI recommendation system by combining Alibaba Cloud Linux, OpenSearch, and AIRec. This architecture deploys a custom inference model on Alibaba Cloud Linux, uses OpenSearch for vector-based candidate retrieval, and leverages AIRec as the orchestration and serving layer.