AWS Machine Learning Blog

Amazon Polly launches a child US English NTTS voice

Amazon Polly turns text into lifelike speech, allowing you to create voice-enabled applications. We’re excited to announce the general availability of a new US English child voice—Kevin. Kevin’s voice was developed using the latest Neural Text-to-Speech (NTTS) technology, making it sound natural and human-like. This voice imitates the voice of a male child. Have a […]

Delivering real-time racing analytics using machine learning

AWS DeepRacer is a fun and easy way for developers with no prior experience to get started with machine learning (ML). At the end of the 2019 season, the AWS DeepRacer League engaged the Amazon ML Solutions Lab to develop a new sports analytics feature for the AWS DeepRacer Championship Cup at re:Invent 2019. The […]

A/B Testing ML models in production using Amazon SageMaker

Amazon SageMaker is a fully managed service that provides developers and data scientists the ability to quickly build, train, and deploy machine learning (ML) models. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thomson Reuters, use Amazon SageMaker to remove the heavy lifting from the ML process. With Amazon SageMaker, […]

Object detection and model retraining with Amazon SageMaker and Amazon Augmented AI

Industries like healthcare, media, and social media platforms use image analysis workflows to identify objects and entities within pictures to understand the whole image. For example, an ecommerce website might use objects present in an image to surface relevant search results. Sometimes image analysis may be difficult when images are blurry or more nuanced. In […]

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 […]

How REA Group implemented automated image compliance with Amazon Rekognition

Amazon Rekognition is a machine learning (ML) based image and vision analysis service that can identify objects, people, text, scenes, and activities in images and videos, and detect any inappropriate content. Amazon Rekognition text detection enables you to recognize and extract textual content from images and videos. For example, in image sharing and social media […]

Introducing Recommendation Filters in Amazon Personalize

This blog post was last reviewed or updated April, 2022 with updates to how filters are configured. Today, we are pleased to announce the addition of Recommendation Filters in Amazon Personalize, which improve the relevance of personalized recommendations by filtering out recommendations for products that users have already purchased, videos they have already watched, or […]

Creating a persistent custom R environment for Amazon SageMaker

Amazon SageMaker is a fully managed service that allows you to build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. In August 2019, Amazon SageMaker announced the availability of the pre-installed R kernel in […]

Coding with R on Amazon SageMaker notebook instances

Many AWS customers already use the popular open-source statistical computing and graphics software environment R for big data analytics and data science. Amazon SageMaker is a fully managed service that lets you build, train, and deploy machine learning (ML) models quickly. Amazon SageMaker removes the heavy lifting from each step of the ML process to […]

Using Amazon SageMaker with Amazon Augmented AI for human review of Tabular data and ML predictions

Tabular data is a primary method to store data across multiple industries, including financial, healthcare, manufacturing, and many more. A large number of machine learning (ML) use cases deal with traditional structured or tabular data. For example, a fraud detection use case might be tabular inputs like a customer’s account history or payment details to […]