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 專家
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.

主題

儲存空間機器學習與 AI 分析

標籤

Amazon Simple Storage Service 建置與訓練 ML 模型 Amazon QuickSight AWS Glue 利用 Amazon SageMaker 和 Kubernetes 進行 ML 操作

語言

English

himanshustallongmailcom

已提問 1 年前檢視次數 264 次

1 個回答

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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.

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Gonzalo Herreros

已回答 1 年前

Architecture review for Custom Anomaly AI

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