AWS Machine Learning Blog

Model Server for Apache MXNet v1.0 released

AWS recently released Model Server for Apache MXNet (MMS) v1.0, featuring a new API for managing the state of the service, which includes the ability to dynamically load models during runtime, to lower latency, and to have higher throughput. In this post, we will explore the new features and showcase the performance gains of the […]

Read More

Using deep learning on AWS to lower property damage losses from natural disasters

Natural disasters like the 2017 Santa Rosa fires and Hurricane Harvey cost hundreds of billions of dollars in property damages every year, wreaking economic havoc in the lives of homeowners. Insurance companies do their best to evaluate affected homes, but it could take weeks before assessments are available and salvaging and protecting the homes can […]

Read More

Amazon Translate now offers 113 new language pairs

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Today, we are launching 113 new language pairs. Customers can now translate between currently supported languages, such as French to Spanish for example, with a single API request. With this update, we are expanding the number of supported language pairs […]

Read More

Understanding Amazon SageMaker notebook instance networking configurations and advanced routing options

An Amazon SageMaker notebook instance provides a Jupyter notebook app through a fully managed machine learning (ML) Amazon EC2 instance. Amazon SageMaker Jupyter notebooks are used to perform advanced data exploration, create training jobs, deploy models to Amazon SageMaker hosting, and test or validate your models. The notebook instance has a variety of networking configurations […]

Read More

Amazon SageMaker Batch Transform now supports Amazon VPC and AWS KMS-based encryption

Amazon SageMaker now supports running Batch Transform jobs in Amazon Virtual Private Cloud (Amazon VPC) and using AWS Key Management Service (AWS KMS). Amazon VPC allows you to control access to your machine learning (ML) model containers and data so that they are private and aren’t accessible over the internet. AWS KMS enables you to encrypt […]

Read More

Use AWS DeepLens to give Amazon Alexa the power to detect objects via Alexa skills

People are using Alexa for all types of activities in their homes, such as checking their bank balances, ordering pizza, or simply listening to their music from their favorite artists. For the most part, the primary interaction with the Echo has been your voice. In this blog post, we’ll show you how to build a […]

Read More

Amazon Comprehend introduces new Region availability and language support for French, German, Italian, and Portuguese

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. The service does the following for you: Identifies the language of the text. Extracts key phrases, places, people, brands, or locations. Understands how positive or negative the text is. Analyzes text using tokenization and parts […]

Read More

Track the number of coffees consumed using AWS DeepLens

AWS DeepLens is a deep-learning-enabled video camera for developers. It enables you to expand your deep learning skillsets through the use of a fully programmable video camera, tutorials, code, and pre-trained models. The goal with this blog post is to show you how to get started with the AWS DeepLens and how this device facilitates the introduction […]

Read More

Shopper Sentiment: Analyzing in-store customer experience

Retailers have been using in-store video to analyze customer behaviors and demographics for many years.  Separate systems are commonly used for different tasks.  For example, one system would count the number of customers moving through a store, in which part of the store those customers linger and near which products.  Another system will hold the store layout, whilst yet […]

Read More

Accelerate model training using faster Pipe mode on Amazon SageMaker

Amazon SageMaker now comes with a faster Pipe mode implementation, significantly accelerating the speeds at which data can be streamed from Amazon Simple Storage Service (S3) into Amazon SageMaker while training machine learning models. Pipe mode offers significantly better read throughput than the File mode that downloads data to the local Amazon Elastic Block Store […]

Read More