Category: Artificial Intelligence

This blog post describes how to train, deploy, and retrieve predictions from a machine learning (ML) model using Amazon SageMaker and R. The model predicts abalone age as measured by the number of rings in the shell. The reticulate package will be used as an R interface to Amazon SageMaker Python SDK to make API calls to Amazon […]

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Support for Amazon Translate API is now available in the AWS Mobile SDK for Android and iOS. Now, you can use the AWS Mobile SDK to develop and publish multilingual mobile apps quickly and easily with Amazon Translate. By […]

Today AWS released Model Server for Apache MXNet (MMS) v0.4, which adds support for serving Gluon models. Gluon is an imperative and dynamic interface for MXNet, which enables rapid model development, while maintaining MXNet performance. With this release, MMS adds support for packaging and serving Gluon models at scale. In this blog post, we will […]

Today, we are introducing Pipe input mode support for the Amazon SageMaker built-in algorithms. With Pipe input mode, your dataset is streamed directly to your training instances instead of being downloaded first. This means that your training jobs start sooner, finish quicker, and need less disk space. Amazon SageMaker algorithms have been engineered to be […]

The Keras-MXNet deep learning backend is available now, thanks to contributors to the Keras and Apache MXNet (incubating) open source projects. Keras is a high-level neural network API written in Python. It’s popular for its fast and easy prototyping of CNNs and RNNs. Keras developers can now use the high-performance MXNet deep learning engine for […]

In this blog post, we conduct a performance comparison for PCA using Amazon SageMaker, Spark ML, and Scikit-Learn on high-dimensional datasets. SageMaker consistently showed faster computational performance. Refer Figures (1) and (2) at the bottom to see the speed improvements. Principal Component Analysis Principal Component Analysis (PCA) is an unsupervised learning algorithm that attempts to […]

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 […]