AWS Machine Learning Blog

Amazon Textract automatically extracts text and data from scanned documents, and goes beyond simple optical character recognition (OCR) to also identify the contents of fields and information in tables, without templates, configuration, or machine learning experience required. Customers such as Intuit, PitchBook, Change Healthcare, Alfresco, and more are already using Amazon Textract to automate their […]

TensorFlow is an open-source machine learning (ML) library widely used to develop heavy-weight deep neural networks (DNNs) that require distributed training using multiple GPUs across multiple hosts. Amazon SageMaker is a managed service that simplifies the ML workflow, starting with labeling data using active learning, hyperparameter tuning, distributed training of models, monitoring of training progression, […]

Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning (ML) quickly. Ground Truth offers easy access to third-party and your own human labelers and provides them with built-in workflows and interfaces for common labeling tasks. Additionally, Ground Truth can lower your labeling costs by up to 70% using automatic labeling, […]

Amazon Polly turns text into lifelike speech for voice-enabled applications. AWS is excited to announce the general availability of all Neural Text-to-Speech (NTTS) voices in the Asia Pacific (Sydney) Region. These voices deliver groundbreaking improvements in speech quality through a new machine learning approach. If you are in the Sydney Region, you can now synthesize […]

Auto manufacturers are continuously adding new controls, interfaces, and intelligence into their vehicles. They publish manuals detailing how to use these functions, but these handbooks are cumbersome. Because they consist of hundreds of pages in several languages, it can be difficult to search for relevant information about specific features. Attempts to replace paper-based manuals with […]

Today at AWS re:Invent 2019, I’m excited to announce the AWS Machine Learning (ML) Embark program to help companies transform their development teams into machine learning practitioners. AWS ML Embark is based on Amazon’s own experience scaling the use of machine learning inside its own operations as well as the lessons learned through thousands of […]

Two of the most popular machine learning models used today are BERT, for natural language processing (NLP), and Mask R-CNN, for image recognition. Over the past several months, AWS has significantly improved the underlying infrastructure, network, machine learning (ML) framework, and model code to achieve the best training time for these two popular state-of-the-art models. […]

We are excited to announce Amazon Transcribe Medical, a new HIPAA-eligible, machine learning automatic speech recognition (ASR) service that allows developers to add medical speech-to-text capabilities to their applications. Transcribe Medical provides accurate and affordable medical transcription, enabling healthcare providers, IT vendors, insurers, and pharmaceutical companies to build services that help physicians, nurses, researchers, and […]

AWS is excited to introduce Amazon SageMaker Operators for Kubernetes in general availability. This new feature makes it easier for developers and data scientists that use Kubernetes to train, tune, and deploy machine learning (ML) models in Amazon SageMaker. You can install these operators on your Kubernetes cluster to create Amazon SageMaker jobs natively using […]

Since the launch of AWS DeepRacer, tens of thousands of developers from around the world have been getting hands-on experience with reinforcement learning in the AWS Management Console, by building their AWS DeepRacer models and competing in the AWS DeepRacer League for a chance to be crowned the 2019 AWS DeepRacer League Champion. The League […]