Category: HAQM SageMaker Ground Truth

In this blog post we’re going to walk you through an example situation where you’ve just built a machine learning system that labels your data at volume and you want to perform manual quality assurance (QA) on some of the labels. How can you do so without overwhelming your limited resources?  We’ll show you how, […]

At re:Invent 2018 we launched HAQM SageMaker Ground Truth, which can Build Highly Accurate Datasets and Reduce Labeling Costs by up to 70% using machine learning. HAQM SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built-in workflows and interfaces for common labeling tasks. Additionally, HAQM SageMaker Ground […]

With HAQM SageMaker Ground Truth, you can easily and inexpensively build more accurately labeled machine learning datasets. To decrease labeling costs, use Ground Truth machine learning to choose “difficult” images that require human annotation and “easy” images that can be automatically labeled with machine learning. This post explains how automated data labeling works and how […]

Data scientists and developers can now easily train machine learning models on datasets labeled by HAQM SageMaker Ground Truth. HAQM SageMaker Training now accepts the labeled datasets produced in augmented manifest format as input through both AWS Management Console and HAQM SageMaker Python SDK APIs. Last month during AWS re:Invent, we launched HAQM SageMaker Ground […]