Category: Amazon SageMaker Ground Truth

Launched at AWS re:Invent 2018, Amazon SageMaker Ground Truth helps you quickly build highly accurate training datasets for your machine learning models. Amazon 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, Amazon SageMaker Ground Truth can lower your […]

Launched at AWS re:Invent 2018, Amazon SageMaker Ground Truth enables you to efficiently and accurately label the datasets required to train machine learning (ML) systems. Ground Truth provides built-in labeling workflows that take human labelers step-by-step through tasks and provide tools to help them produce good results. Built-in workflows are currently available for object detection, […]

Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning. It offers easy access to public and private human labelers, and provides them with built-in workflows and interfaces for common labeling tasks. Ground Truth can lower your labeling costs by up to 70% using automatic labeling. It works by training Ground […]

With Amazon SageMaker Ground Truth, you can build highly accurate training datasets for machine learning quickly. 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, SageMaker Ground Truth can lower your labeling costs by up to 70% using automatic labeling, […]

Good machine learning models are built with large volumes of high-quality training data. But creating this kind of training data is expensive, complicated, and time-consuming. To help a model learn how to make the right decisions, you typically need a human to manually label the training data. Amazon SageMaker Ground Truth provides labeling workflows for […]

Amazon SageMaker Ground Truth helps you quickly build highly accurate training datasets for machine learning (ML). To get your data labeled, you can use your own workers, a choice of vendor-managed workforces that specialize in data labeling, or a public workforce powered by Amazon Mechanical Turk. The public workforce is large and economical but as […]

Amazon SageMaker Ground Truth helps you quickly build highly accurate training datasets for machine learning (ML). You can use your own workers, a choice of vendor-managed workforces that specialize in data labeling, or a public workforce powered by Amazon Mechanical Turk to provide the human-generated labels. To get high-quality labels, you must provide simple, concise, […]

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 Amazon SageMaker Ground Truth, which can Build Highly Accurate Datasets and Reduce Labeling Costs by up to 70% using machine learning. Amazon 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, Amazon SageMaker Ground […]

With Amazon 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 […]