## MLOps for SageMaker Endpoint Deployment

This is a sample code repository for demonstrating how you can organize your code for deploying an realtime inference Endpoint infrastructure. This code repository is created as part of creating a Project in SageMaker. 

This code repository has the code to find the latest approved ModelPackage for the associated ModelPackageGroup and automaticaly deploy it to the Endpoint on detecting a change (`build.py`). This code repository also defines the CloudFormation template which defines the Endpoints as infrastructure. It also has configuration files associated with `staging` and `prod` stages. 

Upon triggering a deployment, the GitLab CI will deploy 2 Endpoints - `staging` and `prod`. After the first deployment is completed, the pipeline waits for a manual approval step for promotion to the prod stage. 

You own this code and you can modify this template to change as you need it, add additional tests for your custom validation. 

A description of some of the artifacts is provided below:


## Layout of the SageMaker ModelBuild Project Template

`.gitlab-ci.yml`
 - this file is used by the GitLab CI YAML file.

`build.py`
 - this python file contains code to get the latest approve package arn and exports staging and configuration files. This is invoked from the Build stage.

`terraform-package`
 - this package has the terraform code to create Sagemaker model endpoints.

`staging-config.json`
 - this configuration file is used to customize `staging` stage in the pipeline. You can configure the instance type, instance count here.

`prod-config.json`
 - this configuration file is used to customize `prod` stage in the pipeline. You can configure the instance type, instance count here.