AWS Machine Learning Blog
Tag: Amazon SageMaker Pipelines
Best practices and design patterns for building machine learning workflows with Amazon SageMaker Pipelines
In this post, we provide some best practices to maximize the value of SageMaker Pipelines and make the development experience seamless. We also discuss some common design scenarios and patterns when building SageMaker Pipelines and provide examples for addressing them.
Unlocking efficiency: Harnessing the power of Selective Execution in Amazon SageMaker Pipelines
MLOps is a key discipline that often oversees the path to productionizing machine learning (ML) models. It’s natural to focus on a single model that you want to train and deploy. However, in reality, you’ll likely work with dozens or even hundreds of models, and the process may involve multiple complex steps. Therefore, it’s important […]
Launch Amazon SageMaker Autopilot experiments directly from within Amazon SageMaker Pipelines to easily automate MLOps workflows
Amazon SageMaker Autopilot, a low-code machine learning (ML) service that automatically builds, trains, and tunes the best ML models based on tabular data, is now integrated with Amazon SageMaker Pipelines, the first purpose-built continuous integration and continuous delivery (CI/CD) service for ML. This enables the automation of an end-to-end flow of building ML models using […]