How to implement a CI / CD model development and evaluation pipeline in AWS Sagemaker.

Van Gogh, Vincent. Starry night. (source)

We are setting up a project at Sagemaker Studio to build our development pipeline.

Set up the sagemaker studio with the quick start option. (Image by author)
Sagemaker control panel. (Image by author)
Create a project option. (Image by author)
Sagemaker project templates. (Image by author)
Project stocks. (Image by author)
Archive file structure. (Image by author)
run-pipeline --module-name pipelines.customer_churn.pipeline 
!aws s3 cp s3://sagemaker-sample-files/datasets/tabular/synthetic/churn.txt ./
import os
import boto3
import sagemaker
prefix = 'sagemaker/DEMO-xgboost-churn'
region = boto3.Session().region_name
default_bucket = sagemaker.session.Session().default_bucket()
role = sagemaker.get_execution_role()
RawData = boto3.Session().resource('s3')
.Bucket(default_bucket).Object(os.path.join(prefix, 'data/RawData.csv'))
print(os.path.join("s3://",default_bucket, prefix, 'data/RawData.csv'))
Make changes and press the code on the remote control. (Image by author)
Your Pipes Performance tab. (Image by author)
Pipe diagram. (Image by author)
The road to success is rough. (Image by author)
Information about your model. (Image by author)
Model approval. (Image by author)

I’ve tried to keep this guide to the extent I use Sagemaker because it’s long anyway and part 2 is still coming. The goal is to give a quick overview of the different parts of Sagemaker by implementing a simple project. I suggest to readers that you don’t follow the instructions step by step and try your own ideas and steps, you often fail, but you learn a lot, and that’s the agenda. I hope you enjoy reading this guide as much as I have enjoyed compiling it. Feel free to drop any suggestions or feedback in the comments, you would like to hear them.


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