AWS Machine Learning Blog
Author: Vikram Madan
Labeling data for 3D object tracking and sensor fusion in Amazon SageMaker Ground Truth
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 […]
Tracking the throughput of your private labeling team through 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 […]
Adding a data labeling workflow for named entity recognition with Amazon SageMaker Ground Truth
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, […]
Use two additional data labeling services for your Amazon SageMaker Ground Truth labeling jobs
We’re excited to announce the availability of two more data labeling services that you can use for your Amazon SageMaker Ground Truth labeling jobs: Data Labeling Services by iMerit’s US-based workforce Data Labeling Services by Startek, Inc. These new listings on the AWS Marketplace supplement the existing iMerit India-based workforce listing to provide you a […]
Apache MXNet Version 0.12 Extends Gluon Functionality to Support Cutting Edge Research
Last week, the Apache MXNet community released version 0.12 of MXNet. The major features were support for NVIDIA Volta GPUs and sparse tensors. The release also included a number of new features for the Gluon programming interface. In particular, these features make it easier to implement cutting-edge research in your deep learning models: Variational dropout, […]
Introducing Gluon — An Easy-to-Use Programming Interface for Flexible Deep Learning
Today, AWS and Microsoft announced a new specification that focuses on improving the speed, flexibility, and accessibility of machine learning technology for all developers, regardless of their deep learning framework of choice. The first result of this collaboration is the new Gluon interface, an open source library in Apache MXNet that allows developers of all skill levels to prototype, […]