CI/CD in AWS

[shouvik19]

MLOps

• MLOPs is derived from DevOps so most of its principles are applicable here as well, but the main objective of MLOPs is to quicker deployments and scale up the ML model over a period of time.

MLOPs with AWS Ecosystem

1. AWS Sagemaker

   • Build, Train and Deploy models in AWS sagemaker.

2. AWS CodeCommit

   • AWS CodeCommit is a version control service that allows you to create your own GIT repositories.

3. AWS CodePipeline

• AWS CodePipeline is a fully managed service for continuous delivery of code changes in a fast and quality manner. It helps you design an end to end pipeline with builds, testing and deployment of the code in an automatic way.
• It also offers a visualization of your pipeline process for easy tracking and monitoring purpose.

4. AWS CodeBuild

• AWS CodeBuild is a fully managed service for building source code, perform test and create packages of code ready to be deployed. It allows you to scale multiple builds simultaneously and it takes care of all the resources in background without you need to worry about it.

5. AWS CodeDeploy

Sources https://www.slideshare.net/AmazonWebServices/cicd-for-your-machine-learning-pipeline-with-amazon-sagemaker-dvc303-aws-reinvent-2018

[shouvik19]

• Sagemaker
  1. Create machine learning model in AWS Sagemaker.
  2. Use Amazon Elastic Kubernetes Service, to deploy and manage containerized applications.