AWS Machine Learning Blog

Learn about Dee: The DeepLens Educating Entertainer – The second place winner of the AWS DeepLens Challenge Hackathon

Matthew Clark is a software developer turned architect. He lives in Manchester in the north of England, and he’s soon to be the proud owner of a new kitchen. He’s also the creator of Dee – the DeepLens Educating Entertainer, which won second place in the AWS DeepLens Challenge. Dee is an example of how […]

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Build a March Madness predictor application supported by Amazon SageMaker

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 […]

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Amazon Polly releases new SSML Breath feature

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 […]

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Create a Word-Pronunciation sequence-to-sequence model using Amazon SageMaker

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 […]

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Mount an EFS file system to an Amazon SageMaker notebook (with lifecycle configurations)

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 […]

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Customize your Amazon SageMaker notebook instances with lifecycle configurations and the option to disable internet access

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, […]

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Predict March Madness using Amazon Sagemaker

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 […]

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Use Amazon CloudWatch custom metrics for real-time monitoring of Amazon Sagemaker model performance

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 […]

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Deploy Gluon models to AWS DeepLens using a simple Python API

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. […]

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