AWS Machine Learning Blog

Catching fraud faster by building a proof of concept in Amazon Fraud Detector

Amazon Fraud Detector is a fully managed service that makes it easy to identify potentially fraudulent online activities, such as the creation of fake accounts or online payment fraud. Unlike general-purpose machine learning (ML) packages, Amazon Fraud Detector is designed specifically to detect fraud. Amazon Fraud Detector combines your data, the latest in ML science, […]

Learn how to select ML instances on the fly in Amazon SageMaker Studio

Amazon Web Services (AWS) is happy to announce the general availability of Notebooks within Amazon SageMaker Studio. Amazon SageMaker Studio supports on-the-fly selection of machine learning (ML) instance types, optimized and pre-packaged Amazon SageMaker Images, and sharing of Jupyter notebooks. You can switch a notebook from using a kernel on one instance type to another, […]

Advance your career with a scholarship to the AWS Machine Learning Engineer Nanodegree program from Udacity

Machine learning (ML) is one of the fastest growing areas in technology and a highly sought-after skill set in today’s job market. The growth of artificial intelligence is expected to create 58 million net new jobs in the next few years, according to the World Economic Forum [1]. However, according to the Tencent Research Institute, […]

Building a smart garage door opener with AWS DeepLens and Amazon Rekognition

Many industries, including retail, manufacturing, and healthcare, are adopting IoT-enabled devices and using AI or machine learning (ML) technologies to enable such devices to make human-like decisions without human intervention. You can also apply some of the use cases involving powering IoT-enabled devices with AI/ML technologies at home. This post showcases how to use AWS […]

ML Explainability with Amazon SageMaker Debugger

Machine Learning (ML) impacts industries around the globe, from financial services industry (FSI) and manufacturing  to autonomous vehicles and space exploration. ML is no longer just an aspirational technology exclusive to academic and research institutions; it has evolved into a mainstream technology that has the potential to benefit organizations of all sizes. However, a lack […]

Build forecasts and find anomalies from your data with Amazon QuickSight ML Insights

As technology is advancing, your business is collecting more and more data from different sources. After collecting so many data points, it is often challenging to find the right insights to help your business grow. Dashboards are great at visualizing your data, based upon how you built them, but not always great at finding hidden […]

Scaling your AI-powered Battlesnake with distributed reinforcement learning in Amazon SageMaker

Battlesnake is an AI competition in which you build AI-powered snakes. Battlesnake’s rules are similar to the traditional snakes game. Your goal is to be the last surviving snake when competing against other snakes. Developers of all levels build snakes using techniques ranging from unique heuristic-based strategies to state-of-the-art deep reinforcement learning (RL) algorithms. You […]

Amazon A2I is now generally available

AWS is excited to announce the general availability of Amazon Augmented AI (Amazon A2I), a new service that makes it easy to implement human reviews of machine learning (ML) predictions at scale. Amazon A2I removes the undifferentiated heavy lifting associated with building and managing expensive and complex human review systems, so you can ensure your […]

Announcing availability of Inf1 instances in Amazon SageMaker for high performance and cost-effective machine learning inference

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. Tens of thousands of customers, including Intuit, Voodoo, ADP, Cerner, Dow Jones, and Thompson Reuters, use Amazon SageMaker to remove the heavy lifting from each step of the […]

Deploying PyTorch models for inference at scale using TorchServe

Many services you interact with today rely on machine learning (ML). From online search and product recommendations to speech recognition and language translation, these services need ML models to serve predictions. As ML finds its way into even more services, you face the challenge of taking the results of your hard work and deploying the […]