AWS Machine Learning Blog

Category: Amazon Simple Storage Service (S3)

Use RStudio on Amazon SageMaker to create regulatory submissions for the life sciences industry

Pharmaceutical companies seeking approval from regulatory agencies such as the US Food & Drug Administration (FDA) or Japanese Pharmaceuticals and Medical Devices Agency (PMDA) to sell their drugs on the market must submit evidence to prove that their drug is safe and effective for its intended use. A team of physicians, statisticians, chemists, pharmacologists, and […]

Cloud-based medical imaging reconstruction using deep neural networks

Medical imaging techniques like computed tomography (CT), magnetic resonance imaging (MRI), medical x-ray imaging, ultrasound imaging, and others are commonly used by doctors for various reasons. Some examples include detecting changes in the appearance of organs, tissues, and vessels, and detecting abnormalities such as tumors and various other type of pathologies. Before doctors can use […]

Machine learning inference at scale using AWS serverless

With the growing adoption of Machine Learning (ML) across industries, there is an increasing demand for faster and easier ways to run ML inference at scale. ML use cases, such as manufacturing defect detection, demand forecasting, fraud surveillance, and many others, involve tens or thousands of datasets, including images, videos, files, documents, and other artifacts. […]

Scan Amazon S3 buckets for content moderation using S3 Batch and Amazon Rekognition

Dealing with content in large scale is often challenging, costly, and a heavy lift operation. The volume of user-generated and third-party content has been increasing substantially in industries like social media, ecommerce, online advertising, and media sharing. Customers may want to review this content to ensure that it follows corporate governance and regulations. But they […]

Train fraudulent payment detection with Amazon SageMaker

The ability to detect fraudulent card payments is becoming increasingly important as the world moves towards a cashless society. For decades, banks have relied on building complex mathematical models to predict whether a given card payment transaction is likely to be fraudulent or not. These models must be both accurate and precise—they must catch fraudulent […]

Announcing the Amazon S3 plugin for PyTorch

November 2023: On 11/22/2023, AWS announced the Amazon S3 Connector for PyTorch ─ a new connector that delivers high throughput for PyTorch training jobs that access data in Amazon S3. We recommend customers use the new connector for PyTorch training jobs that read and write data in Amazon S3. The Amazon S3 Connector for PyTorch […]

Schedule an Amazon SageMaker Data Wrangler flow to process new data periodically using AWS Lambda functions

Data scientists can spend up to 80% of their time preparing data for machine learning (ML) projects. This preparation process is largely undifferentiated and tedious work, and can involve multiple programming APIs and custom libraries. Announced at AWS re:Invent 2020, Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for […]

How Intel Olympic Technology Group built a smart coaching SaaS application by deploying pose estimation models – Part 1

February 9, 2024: Amazon Kinesis Data Firehose has been renamed to Amazon Data Firehose. Read the AWS What’s New post to learn more. The Intel Olympic Technology Group (OTG), a division within Intel focused on bringing cutting-edge technology to Olympic athletes, collaborated with AWS Machine Learning Professional Services (MLPS) to build a smart coaching software […]

Simplify and automate anomaly detection in streaming data with Amazon Lookout for Metrics

Do you want to monitor your business metrics and detect anomalies in your existing streaming data pipelines? Amazon Lookout for Metrics is a service that uses machine learning (ML) to detect anomalies in your time series data. The service goes beyond simple anomaly detection. It allows developers to set up autonomous monitoring for important metrics […]

Intelligent governance of document processing pipelines for regulated industries

Processing large documents like PDFs and static images is a cornerstone of today’s highly regulated industries. From healthcare information like doctor-patient visits and bills of health, to financial documents like loan applications, tax filings, research reports, and regulatory filings, these documents are integral to how these industries conduct business. The mechanisms by which these documents […]