AWS Machine Learning Blog

Category: AWS CodePipeline

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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Create a cross-account machine learning training and deployment environment with AWS Code Pipeline

A continuous integration and continuous delivery (CI/CD) pipeline helps you automate steps in your machine learning (ML) applications such as data ingestion, data preparation, feature engineering, modeling training, and model deployment. A pipeline across multiple AWS accounts improves security, agility, and resilience because an AWS account provides a natural security and access boundary for your […]

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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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