Category: Artificial Intelligence

Amazon SageMaker provides fully managed instances running Jupyter Notebooks for data exploration and preprocessing. Customers really appreciate how easy it is to launch a pre-configured notebook instance with just one click. Today, we are making them more customizable by providing two new options: lifecycle configuration that helps automate the process of customizing your notebook instance, […]

It’s mid-March and in the United States that can mean only one thing – it’s time for March Madness! Every year countless people fill out a bracket trying to pick which college basketball team will take it all. Do you have a favorite team to win in 2018? In this blog post, we’ll show you […]

The training and learning process of deep learning (DL) models can be expensive and time consuming. It’s important for data scientists to monitor the model metrics, such as the training accuracy, training loss, validation accuracy, and validation loss, and make informed decisions based on those metrics. In this blog post, I’ll show you how to […]

Today we are excited to announce that you can deploy your custom models trained using Gluon to your AWS DeepLens. Gluon is an open source deep learning interface which allows developers of all skill levels to prototype, build, train, and deploy sophisticated machine learning models for the cloud, devices at the edge, and mobile apps. […]

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 […]

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.

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 […]

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 […]

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 […]

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 […]