Category: Amazon Machine Learning

Amazon CloudWatch is now available for Ray on Amazon Elastic Compute Cloud (Amazon EC2). Ray is an open source (Apache 2.0 License) framework to build and scale distributed applications. CloudWatch is a monitoring and observability service that provides data and actionable insights to monitor your applications, respond to system-wide performance changes, and optimize resource utilization. […]

Companies providing digital services are looking for ways to effectively identify fraudulent activities, such as online payment fraud and fake account creation. Amazon Fraud Detector is a fully managed service that uses machine learning (ML) and builds on 20 years of fraud detection expertise from Amazon Web Services (AWS) and Amazon.com to automatically identify potentially […]

This post was written by Frank Munz, Staff Developer Advocate at Databricks. An introduction to Delta Sharing During the past decade, much thought went into system and application architectures using domain-driven design and microservices, but we are still on the verge of building distributed data meshes. Such data meshes are based on two fundamental principles: […]

This post was written by Willem Pienaar, Principal Engineer at Tecton and creator of Feast. Feast is an open source feature store and a fast, convenient way to serve machine learning (ML) features for training and online inference. Feast lets you build point-in-time correct training datasets from feature data, allows you to deploy a production-grade […]

Machine learning (ML) algorithms are widely used for computer vision (CV) applications, such as image classification, object detection, and semantic segmentation. With the latest development of the Industrial Internet of Things (IIoT), ML algorithms can be directly implemented at the edge device to process image data and perform anomaly detection, such as for product quality […]

In this post, Josh Izaac (Xanadu) and Eric Kessler (AWS) explain how the open source PennyLane project helps bridge the gap between the quantum computing and machine learning communities. Today, we are announcing that AWS is joining the steering council of the PennyLane open source project for variational quantum computing and quantum machine learning. Our […]

Over the past few years, fast.ai has become one of the most cutting-edge, open source, deep learning frameworks and the go-to choice for many machine learning use cases based on PyTorch. It has not only democratized deep learning and made it approachable to general audiences, but fast.ai has also become a role model on how […]

Machine learning (ML) has become a centerpiece for enterprise transformation. AWS provides a broad and deep set of ML capabilities for builders with all levels of expertise. Developers with no prior ML experience can seamlessly build sophisticated AI-driven applications using AWS AI services. Developers and data scientists can use Amazon SageMaker, a managed machine learning […]

This article was contributed by Josiah Davis, Charles Frenzel, and Chen Wu. TorchServe is a model serving library that makes it easy to deploy and manage PyTorch models at scale in production environments. TorchServe removes the heavy lifting of deploying and serving PyTorch models with Kubernetes. TorchServe is built and maintained by AWS in collaboration […]

Today more and more companies are taking a personalized approach to content and marketing. For example, retailers are personalizing product recommendations and promotions for customers. An important step toward providing personalized recommendations is to identify a customer’s propensity to take action for a certain category. This propensity is based on a customer’s preferences and past […]