Category: Artificial Intelligence

Amazon Polly is a service that turns text into lifelike speech. Using Amazon Polly you can create applications that talk and build entirely new categories of speech-enabled products. Starting today, you can apply to participate in a developer preview that allows you to use eight English (U.S.) Amazon Polly voices to narrate your Alexa skills.

If your skill uses only a single voice today, you can try changing the voice or adding different voices in the right places to provide an even more engaging experience. Developers in the preview can select a different voice for any utterance by constructing output speech using the Structured Speech Markup Language (SSML) and specifying an Amazon Polly voice using the voice tag for free in Alexa skills. Note that SSML tags that are only available through the Amazon Polly service and not through Alexa skills will not be available when you use this new capability.

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with advanced optimizations for TensorFlow 1.8 to deliver higher-performance training for Amazon EC2 C5 and P3 instances. For CPU-based training scenarios, the Amazon Machine Images (AMIs) now include TensorFlow 1.8, built with Intel’s Advanced Vector Instructions (AVX), SSE, and FMA instruction sets to accelerate vector and floating-point computations. The […]

Update 25 JAN 2019: fast.ai has released a new version of their library and MOOC making the following blog post outdated. For the latest instructions on setting up the library and course on a SageMaker Notebook instance please refer to the instructions outlined here: https://course.fast.ai/start_sagemaker.html fast.ai is an organization dedicated to making the power of deep learning accessible […]

Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. But besides streamlining the machine learning (ML) workflow, Amazon SageMaker also provides a serverless, powerful, and easy-to-use compute environment to execute and parallelize a large spectrum of scientific computing […]

In this blog post, I want to continue the theme of demonstrating agility, cost efficiency, and how AWS can help you innovate through your customer analytics practice. Many of you are exploring how AI can enhance their customer 360o initiatives. I’ll demonstrate how targeted campaigns can be driven by machine learning (ML) through solutions that leverage Amazon SageMaker and Amazon Pinpoint.

In this blog post, I’ll show you how to use multiple pre-trained models with Apache MXNet. Why would you want to try multiple models? Why not just pick the one with the best accuracy? As we will see later in the blog post, even though these models have been trained on the same data set and optimized for maximum accuracy, they do behave slightly differently on specific images.

Through the adoption of Amazon Polly, we have taken our customer service to the next level, resulting in higher output and greater productivity.

With recent advances in CPU and GPU technology, training complex and deep neural network models in a few hours is within reach for many state of-the-art deep models. However, when you use a system with such high processing throughput potential, the required data for the processing pipeline must be ready before each iteration.

This blog post shows you how to use the Amazon SageMaker Python SDK local mode on a recently launched multi-GPU notebook instance type to quickly test a large scale image classification model.

The AWS Deep Learning AMIs for Ubuntu and Amazon Linux now come with Chainer 4 and Microsoft Cognitive Toolkit (CNTK) 2.5.1 configured with optimizations for higher performance execution across Amazon EC2 instances.