AWS Machine Learning Blog

Determining the relevance of a sentence when compared to a specific document is essential for many different types of applications across various industries. In this post, we focus on a use case within the healthcare field to help determine the accuracy of information regarding patient health. Frequently, during each patient visit, a new document is […]

Amazon SageMaker Studio is a web-based fully integrated development environment (IDE) where you can perform end-to-end machine learning (ML) development to prepare data and build, train, and deploy models. Like other AWS services, Studio supports a rich set of security-related features that allow you to build highly secure and compliant environments. One of these fundamental […]

Medical providers often need to analyze and dictate patient phone conversations, doctors’ notes, clinical trial reports, and patient health records. By automating transcription, providers can quickly and accurately provide patients with medical conditions, medication, dosage, strength, and frequency. Generic artificial intelligence-based transcription models can be used to transcribe voice to text. However, medical voice data […]

Amazon Forecast now displays the estimated time it takes to complete an in-progress workflow for importing your data, training the predictor, and generating the forecast. You can now manage your time more efficiently and better plan for your next workflow around the estimated time remaining for your in-progress workflow. Forecast uses machine learning (ML) to […]

Amazon Lookout for Vision is a machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. Many enterprise customers want to identify missing components in […]

Data quality is an important topic for virtually all teams and systems deriving insights from data, especially teams and systems using machine learning (ML) models. Supervised ML is the task of learning a function that maps an input to an output based on examples of input-output pairs. For a supervised ML algorithm to effectively learn […]

Want to accelerate machine learning (ML) innovation in your organization? Join us for 30 days of new Amazon SageMaker content designed to help you build, train, and deploy ML models faster. On April 20, we’re kicking off 30 days of hands-on workshops, Twitch sessions, Slack chats, and partner perspectives. Our goal is to connect you […]

You can now control the Amazon Virtual Private Cloud (Amazon VPC) and encryption settings for your Amazon Comprehend APIs using AWS Identity and Access Management (IAM) condition keys, and encrypt your Amazon Comprehend custom models using customer managed keys (CMK) via AWS Key Management Service (AWS KMS). IAM condition keys enable you to further refine […]

Today, we’re pleased to launch Machine Learning Essentials for Business and Technical Decision Makers—a series of three free, on-demand, digital-training courses from AWS Training and Certification. These courses are intended to empower business leaders and technical decision makers with the foundational knowledge needed to begin shaping a machine learning (ML) strategy for their organization, even […]

In preparation for the upcoming Olympic Games, Intel®, an American multinational corporation and one of the world’s largest technology companies, developed a concept around 3D Athlete Tracking (3DAT). 3DAT is a machine learning (ML) solution to create real-time digital models of athletes in competition in order to increase fan engagement during broadcasts. Intel was looking […]