AWS Machine Learning Blog

Category: AWS CodeBuild

Improve your data science workflow with a multi-branch training MLOps pipeline using AWS

In this post, you will learn how to create a multi-branch training MLOps continuous integration and continuous delivery (CI/CD) pipeline using AWS CodePipeline and AWS CodeCommit, in addition to Jenkins and GitHub. I discuss the concept of experiment branches, where data scientists can work in parallel and eventually merge their experiment back into the main […]

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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 […]

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Using the Amazon SageMaker Studio Image Build CLI to build container images from your Studio notebooks

The new Amazon SageMaker Studio Image Build convenience package allows data scientists and developers to easily build custom container images from your Studio notebooks via a new CLI. The new CLI eliminates the need to manually set up and connect to Docker build environments for building container images in Amazon SageMaker Studio. Amazon SageMaker Studio […]

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