Category: HAQM Simple Storage Service (S3)

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 […]

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

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, HAQM SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for […]

February 9, 2024: HAQM Kinesis Data Firehose has been renamed to HAQM 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 […]

Do you want to monitor your business metrics and detect anomalies in your existing streaming data pipelines? HAQM 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 […]

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 […]

April 2025: This post was reviewed and updated for accuracy. The HAQM SageMaker Studio Image Build convenience package allows data scientists and developers to easily build custom container images from your Studio JupyterLab notebooks via CLI. The CLI eliminates the need to manually set up and connect to Docker build environments for building container images […]

In this blog post, we will show you how to get started building rich text analytics views from your database, without having to learn anything about machine learning for natural language processing models. We’ll do this by leveraging HAQM Comprehend, paired with HAQM Aurora-MySQL and AWS Lambda.