AWS Machine Learning Blog

Category: AWS CloudFormation

Creating an end-to-end application for orchestrating custom deep learning HPO, training, and inference using AWS Step Functions

Amazon SageMaker hyperparameter tuning provides a built-in solution for scalable training and hyperparameter optimization (HPO). However, for some applications (such as those with a preference of different HPO libraries or customized HPO features), we need custom machine learning (ML) solutions that allow retraining and HPO. This post offers a step-by-step guide to build a custom deep […]

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Rust detection using machine learning on AWS

Visual inspection of industrial environments is a common requirement across heavy industries, such as transportation, construction, and shipbuilding, and typically requires qualified experts to perform the inspection. Inspection locations can often be remote or in adverse environments that put humans at risk, such as bridges, skyscrapers, and offshore oil rigs. Many of these industries deal […]

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Creating Amazon SageMaker Studio domains and user profiles using AWS CloudFormation

February 2021 Update: Customers can now use native AWS CloudFormation code templates to model the infrastructure set up for Amazon SageMaker Studio and configure its access for users in their organizations at scale. For more information, please see the announcement post.  Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning […]

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