AWS Machine Learning Blog

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 […]

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. Today we are excited to announce that you can […]

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.

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. At AWS re:Invent 2017 we announced the AWS DeepLens […]

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 […]

April 2023 Update: Starting January 31, 2024, you will no longer be able to access AWS DeepLens through the AWS management console, manage DeepLens devices, or access any projects you have created. To learn more, refer to these frequently asked questions about AWS DeepLens end of life. We are excited to launch a new feature for […]