AWS Machine Learning Blog
Category: Artificial Intelligence
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 […]
Read MoreLearn about ReadToMe – The first place winner of the AWS DeepLens Challenge Hackathon
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 […]
Read MoreCustomize 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, […]
Read MorePredict 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 […]
Read MoreUse 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 […]
Read MoreDeploy 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. […]
Read MoreTrain 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 […]
Read MoreAmazon 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.
Read MoreAnnouncing 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 […]
Read MoreAWS 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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