AWS Machine Learning Blog

Building machine learning workflows with AWS Data Exchange and Amazon SageMaker

Thanks to cloud services such as Amazon SageMaker and AWS Data Exchange, machine learning (ML) is now easier than ever. This post explains how to build a model that predicts restaurant grades of NYC restaurants using AWS Data Exchange and Amazon SageMaker. We use a dataset of 23,372 restaurant inspection grades and scores from AWS […]

Building a custom classifier using Amazon Comprehend

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to find insights and relationships in texts. Amazon Comprehend identifies the language of the text; extracts key phrases, places, people, brands, or events; and understands how positive or negative the text is. For more information about everything Amazon Comprehend can do, […]

Using Amazon Lex Conversation logs to monitor and improve interactions

As a product owner for a conversational interface, understanding and improving the user experience without the corresponding visibility or telemetry can feel like driving a car blindfolded. It is important to understand how users are interacting with your bot so that you can continuously improve the bot based on past interactions. You can gain these […]

Amazon Textract becomes PCI DSS certified, and retrieves even more data from tables and forms

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 […]

Running distributed TensorFlow training with Amazon SageMaker

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, […]

Auto-segmenting objects when performing semantic segmentation labeling with Amazon SageMaker Ground Truth

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 Neural Text-to-Speech voices now available in Sydney Region

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 […]

Building an AR/AI vehicle manual using Amazon Sumerian and Amazon Lex

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 […]

AWS announces the Machine Learning Embark program to help customers train their workforce in machine learning

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 […]

Amazon Web Services achieves fastest training times for BERT and Mask R-CNN

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. […]