DaaS / Products / ML Embedding Pipeline with Vector Search

ML Embedding Pipeline with Vector Search

Use PAI to preprocess training datasets and train embedding models, then store the generated vector embeddings into OSS vector indexes to power a semantic similarity search service end-to-end.

Products involved

Scenario

Use PAI to preprocess training datasets and train embedding models, then store the generated vector embeddings into OSS vector indexes to power a semantic similarity search service end-to-end.

How the products combine

pai · pai-manage-data — Platform for AI (PAI) — Manage and process training datasets

See pai/pai-manage-data.

oss · oss-manage-data — Object Storage Service — Manage vector data and indexes

See oss/oss-manage-data.

Typical questions

build semantic search pipeline
train embeddings and store vectors
ML model to vector search
PAI embeddings to OSS vector index
训练嵌入模型并存入向量索引
PAI数据预处理后生成向量
end-to-end vector retrieval pipeline
从数据训练到向量检索

FAQ

Q: How do I build an end-to-end semantic search pipeline that trains embeddings and stores them in a vector index? A: You can build this pipeline by using PAI to preprocess training datasets and train embedding models, then storing the generated vector embeddings into OSS vector indexes. This cross-product combination powers a complete semantic similarity search service.