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Build a CI/CD pipeline for deploying custom machine learning models using AWS services

 Build a CI/CD pipeline for deploying custom machine learning models using AWS services

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality ML artifacts. AWS Serverless Application Model (AWS SAM) is an open-source framework for building serverless applications. It provides shorthand syntax to express functions, APIs, databases, event source mappings, steps in AWS Step Functions, and more.

Generally, ML workflows orchestrate and automate sequences of ML tasks. A workflow includes data collection, training, testing, human evaluation of the ML model, and deployment of the models for inference.

For continuous integration and continuous delivery (CI/CD) pipelines, AWS recently released Amazon SageMaker Pipelines, the first purpose-built, easy-to-use CI/CD service for ML. Pipelines is a native workflow orchestration tool for building ML pipelines that takes advantage of direct SageMaker integration. For more information, see Building, automating, managing, and scaling ML workflows using Amazon SageMaker Pipelines.

In this post, I show you an extensible way to automate and deploy custom ML models using service integrations between Amazon SageMaker, Step Functions, and AWS SAM using a CI/CD pipeline.

To build this pipeline, you also need to be familiar with the following AWS services:

  • AWS CodeBuild – A fully managed continuous integration service that compiles source code, runs tests, and produces software packages that are ready to deploy
  • AWS CodePipeline – A fully managed continuous delivery service that helps you automate your release pipelines
  • Amazon Elastic Container Registry (Amazon ECR) – A container registry
  • AWS Lambda – A service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume
  • Amazon Simple Storage Service (Amazon S3) – An object storage service that offers industry-leading scalability, data availability, security, and performance
  • AWS Step Functions – A serverless function orchestrator that makes it easy to sequence AWS Lambda functions and multiple AWS services

Solution overview

The solution has two main sections:

  • Use AWS SAM to create a Step Functions workflow with SageMaker – Step Functions recently announced native service integrations with SageMaker. You can use this feature to train ML models, deploy ML models, test results, and expose an inference endpoint. This feature also provides a way to wait for human approval before the state transitions can progress towards the final ML model inference endpoint’s configuration and deployment.
  • Deploy the model with a CI/CD pipeline – One of the requirements of Sag
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Source - Continue Reading: https://aws.amazon.com/blogs/machine-learning/build-a-ci-cd-pipeline-for-deploying-custom-machine-learning-models-using-aws-services/

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