Deepfake Detection MLOps - Build End-to-End Pipeline using Sagemaker Pipeline, AWS CodeBuild and AWS CodePipeline - Part 2

The project is created based on the SageMaker Project Template - MLOps template for model building, training and deployment with third-party Git repositories.

Overall Architecture

The overall architecture of the project is shown below:

The architecture is divided into two main parts:

• The first part involves processing data, training and testing models, and registering the models in the SageMaker Model Registry (as described in this link).
• The second part involves automating the deployment of models from the SageMaker Model Registry to SageMaker endpoints for real-time inference. When a new model version is registered and approved, it initiates a deployment automatically (in this repository).

MLOps for SageMaker Endpoint Deployment

This code repository contains the code to automatically locate and deploy the latest approved ModelPackage for the associated ModelPackageGroup to the Endpoint upon detecting a change (in build.py). The repository also includes a CloudFormation template defining the Endpoints as infrastructure, as well as configuration files for the staging and prod stages.

Upon triggering a deployment, the CodePipeline pipeline will deploy 2 Endpoints - staging and prod. After the first deployment is completed, the CodePipeline waits for a manual approval step for promotion to the prod stage. We will need to go to CodePipeline AWS Managed Console to complete this step.

The figure below shows the model that has been deployed to the staging environment.

Project layout

buildspec.yml

• this file is used by the CodePipeline's Build stage to build a CloudFormation template.

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.

endpoint-config-template.yml

• this CloudFormation template file is packaged by the build step in the CodePipeline and is deployed in different stages.

staging-config.json

• this configuration file is used to customize staging stage in the pipeline.

prod-config.json

• this configuration file is used to customize prod stage in the pipeline.

test\buildspec.yml

• this file is used by the CodePipeline's staging stage to run the test code of the following python file

test\test.py

• this python file contains code to describe and invoke the staging endpoint.