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