AWS Machine Learning Blog

Category: Artificial Intelligence

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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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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 […]

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Amazon SageMaker Neural Topic Model now supports auxiliary vocabulary channel, new topic evaluation metrics, and training subsampling

In this blog post, we introduce three new features of the Amazon SageMaker Neural Topic Model (NTM) that are designed to help improve user productivity, enhance topic evaluation capability, and speed up model training. In addition to these new features, by optimizing sparse operations and the parameter server, we have improved the speed of the […]

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Beyond text: How Spokata uses Amazon Polly to make news and information universally accessible as real-time audio

Just as television transitioned from black and white to color, the web has been moving from a text-based medium to one dominated by sound and vision. Accordingly, content creation has both exploded and changed. Publishers of all types are struggling through this transition as they try to meet the demands of users while keeping their business models intact.

On-demand audio is attracting significant interest from publishers as mobile listening grows and in-car technology begins to disrupt traditional radio. This trend is most visible in the mainstream adoption of podcasts. But podcasts are just the beginning of a rapidly emerging, and diverse, ecosystem of new digital audio formats. Amazon Echo and advanced text-to-speech services such as Amazon Polly are enabling the creation of these new audio products.

In this blog post we describe how Spokata leverages these Amazon technologies to make text-based news and information universally accessible as real-time audio.

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Segmenting brain tissue using Apache MXNet with Amazon SageMaker and AWS Greengrass ML Inference – Part 2

In Part 1 of this blog post, we demonstrated how to train and deploy neural networks to automatically segment brain tissue from an MRI scan in a simple, streamlined way using Amazon SageMaker. We used Apache MXNet to train a convolutional neural network (CNN) on Amazon SageMaker using the Bring Your Own Script paradigm. We […]

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How to use common workflows on Amazon SageMaker notebook instances

Amazon SageMaker notebook instances provide a scalable cloud based development environment to do data science and machine learning. This blog post will show common workflows to make you more productive and effective. The techniques in this blog post will give you tools to treat your notebook instances in a more cloud native way, remembering that […]

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