yunusulucay / feature-engineering-aws

An end-to-end feature engineering process on AWS. SageMaker, S3 Bucket, Lambda and ECR services used.

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Feature Engineering using AWS Services

An end-to-end feature engineering process on AWS. SageMaker, S3 Bucket, Lambda and ECR services used.

Work Flow

eda-schema

Steps

1- Some feature engineering processes implemented on SageMaker notebook.(In notebook)

2- To make feature engineering processes created a SageMaker Processing Job.(In notebook)

3- An end-to-end feature engineering lifecycle will create.

a- The data send to s3 bucket.

b- On s3 a trigger created.(When an event happens send a trigger to lambda)

c- Lambda function created.(To get action. -Not completed)

d- A docker image will create and send to ECR.(-Not completed)

e- With this image on ECR i will create a SageMaker endpoint to direct lambda to.(-Not completed)

f- After this endpoint triggered, input data will go through some process and train,test and val datasets will created.(-Not completed)

About

An end-to-end feature engineering process on AWS. SageMaker, S3 Bucket, Lambda and ECR services used.


Languages

Language:Jupyter Notebook 94.0%Language:Python 6.0%