I am trying to build a architecture for custom anomaly ai on AWS for my startup. Please let me know if my way of thinking is correct or not 1. Data Ingestion: Ingesting the data into AWS S3 in JSON format 2. Data Preprocessing: Converting JSON format to Excel format using AWS Glue and storing it back into S3 3. ML : Using AWS Sagemaker to learn from the Excel format created 4. Quicksight to show the data for data visualization is my way of thinking correct here?

Architecture review for Custom Anomaly AI

I am trying to build a architecture for custom anomaly ai on AWS for my startup. Please let me know if my way of thinking is correct or not

Data Ingestion: Ingesting the data into AWS S3 in JSON format
Data Preprocessing: Converting JSON format to Excel format using AWS Glue and storing it back into S3
ML : Using AWS Sagemaker to learn from the Excel format created
Quicksight to show the data for data visualization

is my way of thinking correct here?

Antonio_Lagrotteria 专家
1 年前
May I ask why converting json to excel?
himanshustallongmailcom
1 年前
it is just my way of thinking. I am open ears to learn in case you have any other idea. But I want to implement Custom Anomaly AI detection.

主题

存储机器学习和人工智能分析

标签

亚马逊简单存储服务 (S3)构建和训练 ML 模型 Amazon QuickSight AWS Glue 使用亚马逊 SageMaker 和 Kubernetes 的 ML Ops

语言

English

himanshustallongmailcom

已提问 1 年前275 查看次数

1 回答

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I don't see the point of converting to Excel if you are not going to use a spreadsheet editor to open but instead using Quicksight. Any other format like CSV will allow you better support and parallelism.

专家

Gonzalo Herreros

已回答 1 年前

Architecture review for Custom Anomaly AI

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