AWS Machine Learning Blog

Build a serverless Twitter reader using AWS Fargate

In a previous post, Ben Snively and Viral Desai showed us how to build a social media dashboard using serverless technology. The social media dashboard reads tweets with the #AWS hashtag, uses machine learning based services to do translation, and natural language processing (NLP) to determine topics, entities, and sentiment analysis. Finally, it aggregates this […]

Read More

Anomaly detection on Amazon DynamoDB Streams using the Amazon SageMaker Random Cut Forest algorithm

Have you considered introducing anomaly detection technology to your business? Anomaly detection is a technique used to identify rare items, events, or observations which raise suspicion by differing significantly from the majority of the data you are analyzing.  The applications of anomaly detection are wide-ranging including the detection of abnormal purchases or cyber intrusions in […]

Read More

Announcing the Winners of the 2018 AWS AI Hackathon

We’re excited to announce the winners of the 2018 AWS AI Hackathon.  Horacio Canales has won first place with his “Second Alert” project. This project enables users from around the world to identify missing persons, including human trafficking victims, children too young to remember their family members’ names, and mentally handicapped individuals. Horacio built the […]

Read More

Amazon SageMaker now comes with new capabilities for accelerating machine learning experimentation

Data scientists and developers can now quickly and easily organize, track, and evaluate their machine learning (ML) model training experiments on Amazon SageMaker. We are introducing a new Amazon SageMaker Search capability that lets you find and evaluate the most relevant model training runs from the hundreds and thousands of your Amazon SageMaker model training […]

Read More

Amazon SageMaker notebooks now support Git integration for increased persistence, collaboration, and reproducibility

It’s now possible to associate GitHub, AWS CodeCommit, and any self-hosted Git repository with Amazon SageMaker notebook instances to easily and securely collaborate and ensure version-control with Jupyter Notebooks. In this blog post, I’ll elaborate on the benefits of using Git-based version-control systems and how to set up your notebook instances to work with Git repositories. Data […]

Read More

Semantic Segmentation algorithm is now available in Amazon SageMaker

Amazon SageMaker is a managed and infinitely scalable machine learning (ML) platform. With this platform, it is easy to build, train, and deploy machine learning models. Amazon SageMaker already has two popular built-in computer vision algorithms for image classification and object detection. The Amazon SageMaker image classification algorithm learns to categorize images into a set of […]

Read More

Introducing Amazon Translate Custom Terminology

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Today, we are introducing Custom Terminology, a feature that customers can use to customize Amazon Translate output to use company- and domain-specific vocabulary. By uploading and invoking Custom Terminology with translation requests, customers have the ability to ensure that their […]

Read More

Introducing medical language processing with Amazon Comprehend Medical

We are excited to announce Amazon Comprehend Medical, a new HIPAA-eligible machine learning service that allows developers to process unstructured medical text and identify information such as patient diagnosis, treatments, dosages, symptoms and signs, and more. Comprehend Medical helps health care providers, insurers, researchers, and clinical trial investigators as well as health care IT, biotech, […]

Read More

Introducing Dynamic Training for deep learning with Amazon EC2

Today we are excited to announce the availability of Dynamic Training (DT) for deep learning models, or DT for short. DT allows deep learning practitioners to reduce model training cost and time by leveraging the cloud’s elasticity and economies of scale. Our first reference implementation of DT is based on Apache MXNet, and is open sourced […]

Read More

Amazon’s own ‘Machine Learning University’ now available to all developers

Today, I’m excited to share that, for the first time, the same machine learning courses used to train engineers at Amazon are now available to all developers through AWS. We’ve been using machine learning across Amazon for more than 20 years. With thousands of engineers focused on machine learning across the company, there are very […]

Read More