AWS Machine Learning Blog

Category: Artificial Intelligence

Train and host Scikit-Learn models in Amazon SageMaker by building a Scikit Docker container

Introduced at re:Invent 2017, Amazon SageMaker provides a serverless data science environment to build, train, and deploy machine learning models at scale. Customers also have the ability to work with frameworks they find most familiar, such as Scikit learn. In this blog post, we’ll accomplish two goals: First, we’ll give you a high-level overview of […]

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Amazon Polly powers Nexmo’s next-gen text-to-speech use cases

As a cloud communications provider that allows businesses to integrate communications capabilities into their applications, Nexmo, the Vonage API Platform, needed a text-to-speech (TTS) solution to help deliver the many synthesized speech use cases we enable for our customers. The solution that we chose had to meet our technological requirements and product philosophy to power Nexmo’s global TTS offerings.

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Announcing the winners of the AWS DeepLens Challenge

At AWS re:Invent 2017 we announced the AWS DeepLens Challenge in conjunction with Intel. The AWS DeepLens Challenge gave attendees of the re:Invent DeepLens workshops an opportunity to put their skills to the test by building a machine learning (ML) project using their AWS DeepLens. The mission was to get creative with computer vision and […]

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AWS Deep Learning AMIs now support Chainer and latest versions of PyTorch and Apache MXNet

The AWS Deep Learning AMIs provide fully-configured environments so that artificial intelligence (AI) developers and data scientists can quickly get started with deep learning models. The Amazon Machine Images (AMIs) now include Chainer (v3.4.0), a flexible and intuitive deep learning (DL) framework, as well as the latest versions of Apache MXNet and PyTorch. The Chainer define-by-run […]

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Amazon SageMaker support for TensorFlow 1.5, MXNet 1.0, and CUDA 9

Amazon SageMaker pre-built deep learning framework containers now support TensorFlow 1.5 and Apache MXNet 1.0, both of which take advantage of CUDA 9 optimizations for faster performance on SageMaker ml.p3 instances. In addition to performance benefits, this provides access to updated features such as Eager execution in TensorFlow and advanced indexing for NDArrays in MXNet. More […]

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Build an online compound solubility prediction workflow with AWS Batch and Amazon SageMaker

Machine learning (ML) methods for the field of computational chemistry are growing at an accelerated rate. Easy access to open-source solvers (such as TensorFlow and Apache MXNet), toolkits (such as RDKit cheminformatics software), and open-scientific initiatives (such as DeepChem) makes it easy to use these frameworks in daily research. In the field of chemical informatics, many […]

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Build your own object classification model in SageMaker and import it to DeepLens

We are excited to launch a new feature for AWS DeepLens that allows you to import models trained using Amazon SageMaker directly into the AWS DeepLens console with one click. This feature is available as of AWS DeepLens software version 1.2.3. You can update your AWS DeepLens software by re-booting your device or by using […]

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Amazon Polly plugin for WordPress now preinstalled on Bitnami AMIs

On February 8, we released the Amazon Polly plugin for WordPress, which enables you to easily voice content and publish podcasts directly from your website. By leveraging audio, you can provide your readers with an alternative way to consume your content and meet the needs of a larger audience. Today, we’re excited to announce that […]

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Create softer speech with the new Amazon Polly phonation tag

Speech Synthesis Markup Language (SSML) is a standardized markup language that enables developers to modify Text-to-Speech (TTS) audio. With SSML, you can control various vocal characteristics of TTS output, such as pronunciation, speech rate, and other elements, to produce a more natural-sounding voice experience. Today, we are excited to announce a new phonation SSML tag […]

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Enhance Your Amazon Lex Chatbots with Responses

You can now add responses to your Amazon Lex chatbots directly from the AWS Management Console. Use responses to set up dynamic, engaging interactions with your users. Using responses Responses are the final element of a bot’s intent, and are displayed to users after the fulfillment of the intent is complete.  A response might include […]

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