Category: Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth Plus helps you prepare high-quality training datasets by removing the undifferentiated heavy lifting associated with building data labeling applications and managing the labeling workforce. All you do is share data along with labeling requirements, and Ground Truth Plus sets up and manages your data labeling workflow based on these requirements. From […]

This is a joint post co-written by AWS and Voxel51. Voxel51 is the company behind FiftyOne, the open-source toolkit for building high-quality datasets and computer vision models. A retail company is building a mobile app to help customers buy clothes. To create this app, they need a high-quality dataset containing clothing images, labeled with different […]

Recent large language models (LLMs) have enabled tremendous progress in natural language understanding. However, they are prone to generating confident but nonsensical explanations, which poses a significant obstacle to establishing trust with users. In this post, we show how to incorporate human feedback on the incorrect reasoning chains for multi-hop reasoning to improve performance on […]

Bounding box annotation is a time-consuming and tedious task that requires annotators to create annotations that tightly fit an object’s boundaries. Bounding box annotation tasks, for example, require annotators to ensure that all edges of an annotated object are enclosed in the annotation. In practice, creating annotations that are precise and well-aligned to object edges […]

Amazon SageMaker Ground Truth Plus is a managed data labeling service that makes it easy to label data for machine learning (ML) applications. One common use case is semantic segmentation, which is a computer vision ML technique that involves assigning class labels to individual pixels in an image. For example, in video frames captured by […]

In this two-part series, we demonstrate how to label and train models for 3D object detection tasks. In part 1, we discuss the dataset we’re using, as well as any preprocessing steps, to understand and label data. In part 2, we walk through how to train a model on your dataset and deploy it to […]

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. This has led to the emergence of a data-centric approach to ML and various techniques to improve model performance by focusing on data requirements. Applying these techniques allows ML practitioners […]

Launched at AWS re:Invent 2021, Amazon SageMaker Ground Truth Plus helps you create high-quality training datasets by removing the undifferentiated heavy lifting associated with building data labeling applications and managing the labeling workforce. All you do is share data along with labeling requirements, and Ground Truth Plus sets up and manages your data labeling workflow […]

Amazon Lookout for Vision provides a machine learning (ML)-based anomaly detection service to identify normal images (i.e., images of objects without defects) vs anomalous images (i.e., images of objects with defects), types of anomalies (e.g., missing piece), and the location of these anomalies. Therefore, Lookout for Vision is popular among customers that look for automated […]

Machine learning (ML) has improved business across industries in recent years—from the recommendation system on your Prime Video account, to document summarization and efficient search with Alexa’s voice assistance. However, the question remains of how to incorporate this technology into your business. Unlike traditional rule-based methods, ML automatically infers patterns from data so as to […]