Category: Artificial Intelligence

What an opening round of March Madness basketball tournament games! We had a buzzer beater, some historic upsets, and exciting games throughout. The model built in our first blog post (Part 1) pointed out a few likely upset candidates (Loyola IL, Butler), but did not see some coming (Marshall, UMBC). I’m sure there will be […]

Natural human speech frequently includes audible breathing sounds as a speaker inhales or exhales during normal speaking. For example, when we speak, we generally take a breath at major pauses. Narrations without breathing sounds produced by Text-to-Speech (TTS) engines often the lack naturalness of a human narrator. Most TTS systems don’t include respiratory sounds in […]

Amazon SageMaker seq2seq offers you a very simple way to make use of the state-of-the-art encoder-decoder architecture (including the attention mechanism) for your sequence to sequence tasks. You just need to prepare your sequence data in recordio-protobuf format and your vocabulary mapping files in JSON format. Then you need to upload them to Amazon Simple […]

In this blog post, we’ll show you how you can mount an Amazon Elastic File System (EFS) to your Amazon SageMaker notebook instance. This is an easy way to store and access large datasets, and to share machine learning scripts from your SageMaker notebook instance. Amazon SageMaker notebooks provide fast access to your own instance running […]

When Alex Schultz first heard about the AWS DeepLens workshop in Dr. Matt Wood’s keynote address at re:Invent 2017, little did he know that a few months later he would be the first place winner of the AWS DeepLens Challenge Hackathon, owe his kids $400, and be the star of a blog post on the […]

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 […]