Category: Amazon SageMaker Ground Truth

Prior to using any kind of supervised machine learning (ML) algorithm, data has to be labeled. Amazon SageMaker Ground Truth simplifies and accelerates this task. Ground Truth uses pre-defined templates to assign labels that classify the content of images or videos or verify existing labels. Ground Truth allows you to define workflows for labeling various […]

Amazon SageMaker Ground Truth is a fully managed data labeling service that makes it easy to build highly accurate training datasets for machine learning. It offers easy access to Amazon Mechanical Turk and private human labelers, and provides them with built-in workflows and interfaces for common labeling tasks. A labeling team may wish to use […]

High-quality machine learning (ML) models depend on accurately labeled, high-quality training, validation, and test data. As ML and deep learning models are increasingly integrated into production environments, it’s becoming more important than ever to have customizable, real-time data labeling pipelines that can continuously receive and process unlabeled data. For example, you may want to create […]

This post is co-written by Chiranjeev Ghai, ML Engineer at zomato. zomato is a global food-tech company based in India. Are you the kind of person who has very specific cravings? Maybe when the mood hits, you don’t want just any kind of Indian food—you want Chicken Chettinad with a side of paratha, and nothing […]

Computer vision uses machine learning (ML) to build applications that process images or videos. With Amazon Rekognition, you can use pre-trained computer vision models to identify objects, people, text, activities, or inappropriate content. Our customers have use cases that span every industry, including media, finance, manufacturing, sports, and technology. Some of these use cases require […]

Object detection is a common task in computer vision (CV), and the YOLOv3 model is state-of-the-art in terms of accuracy and speed. In transfer learning, you obtain a model trained on a large but generic dataset and retrain the model on your custom dataset. One of the most time-consuming parts in transfer learning is collecting […]

Update Aug 12, 2020 – New features: Amazon Comprehend adds five new languages(Spanish, French, German, Italian and Portuguese) read here. Amazon Comprehend increased the limit of number of entities per custom entity model from 12 to 25 read here. Organizations across industries have a lot of unstructured data that you can evaluate to get entity-based […]

As a data scientist attempting to solve a problem using supervised learning, you usually need a high-quality labeled dataset before starting your model building. Amazon SageMaker Ground Truth makes dataset building for a different range of tasks, like text classification and object detection, easier and more accessible to everyone. Ground Truth also helps you build […]

Update October 2020: Amazon Comprehend now supports Amazon SageMaker GroundTruth to help label your datasets for Comprehend’s Custom Model training. For Custom EntityRecognizer, checkout Annotations documentation for more details. For Custom MultiClass and MultiLabel Classifier, checkout MultiClass and MultiLabel documentation for more details respectively. Named entity recognition (NER) involves sifting through text data to locate noun phrases […]

Amazon SageMaker Ground Truth now supports labeling 3D point cloud data. For more information about the launched feature set, see this AWS News Blog post. In this blog post, we specifically cover how to perform the required data transformations of your 3D point cloud data to create a labeling job in SageMaker Ground Truth for […]