Vertex AI GCP 中特征存储和数据集的区别
[英]Difference between Feature Store and Datasets in Vertex AI GCP
问题描述
What is the difference between Feature Store and Dataset inside of Vertex AI (GCP)? 
Vertex AI (GCP) 中的特征存储和数据集有什么区别？ And why the Feature Store has Offline and Online serving nodes?为什么 Feature Store 有 Offline 和 Online 服务节点？ What is it for?它是做什么用的？
1 个解决方案
解决方案1
0 2022-12-01 20:35:30
As described at the official documentation of Vertex AI's Feature Store , a feature store is a container for organizing, storing, and serving ML feature.正如Vertex AI 的 Feature Store的官方文档中所述，特征存储是用于组织、存储和提供 ML 功能的容器。 Basically its a more organized container that can be easily store or share features to permitted users.基本上它是一个更有条理的容器，可以轻松地存储或共享功能给允许的用户。 I would suggest reading the article linked above.我建议阅读上面链接的文章。
Online serving nodes is best described here :最好在这里描述在线服务节点：
"Online serving nodes provide the compute resources used to store and serve feature values for low-latency online serving." “在线服务节点提供用于存储和服务低延迟在线服务的特征值的计算资源。”
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