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?

alatech EXPERT
11 days ago
May I ask why converting json to excel?
himanshustallongmailcom
11 days ago
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.

Topics