AWS Machine Learning Blog

Category: AWS re:Invent

Using model attributes to track your training runs on Amazon SageMaker

With a few clicks in the Amazon SageMaker console or a few one-line API calls, you can now quickly search, filter, and sort your machine learning (ML) experiments using key model attributes, such as hyperparameter values and accuracy metrics, to help you more quickly identify the best models for your use case and get to […]

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Amazon SageMaker notebooks now support Git integration for increased persistence, collaboration, and reproducibility

It’s now possible to associate GitHub, AWS CodeCommit, and any self-hosted Git repository with Amazon SageMaker notebook instances to easily and securely collaborate and ensure version-control with Jupyter Notebooks. In this blog post, I’ll elaborate on the benefits of using Git-based version-control systems and how to set up your notebook instances to work with Git repositories. Data […]

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Semantic Segmentation algorithm is now available in Amazon SageMaker

Amazon SageMaker is a managed and infinitely scalable machine learning (ML) platform. With this platform, it is easy to build, train, and deploy machine learning models. Amazon SageMaker already has two popular built-in computer vision algorithms for image classification and object detection. The Amazon SageMaker image classification algorithm learns to categorize images into a set of […]

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Introducing Amazon Translate Custom Terminology

Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Today, we are introducing Custom Terminology, a feature that customers can use to customize Amazon Translate output to use company- and domain-specific vocabulary. By uploading and invoking Custom Terminology with translation requests, customers have the ability to ensure that their […]

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Introducing medical language processing with Amazon Comprehend Medical

We are excited to announce Amazon Comprehend Medical, a new HIPAA-eligible machine learning service that allows developers to process unstructured medical text and identify information such as patient diagnosis, treatments, dosages, symptoms and signs, and more. Comprehend Medical helps health care providers, insurers, researchers, and clinical trial investigators as well as health care IT, biotech, […]

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Introducing Dynamic Training for deep learning with Amazon EC2

Today we are excited to announce the availability of Dynamic Training (DT) for deep learning models, or DT for short. DT allows deep learning practitioners to reduce model training cost and time by leveraging the cloud’s elasticity and economies of scale. Our first reference implementation of DT is based on Apache MXNet, and is open sourced […]

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Amazon’s own ‘Machine Learning University’ now available to all developers

Today, I’m excited to share that, for the first time, the same machine learning courses used to train engineers at Amazon are now available to all developers through AWS. We’ve been using machine learning across Amazon for more than 20 years. With thousands of engineers focused on machine learning across the company, there are very […]

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Your Guide to AI and Machine Learning at re:Invent 2018

re:Invent 2018 is almost here! As you plan your agenda, artificial intelligence (AI) is undoubtedly a hot topic on your list. This year we have a lot of great technical content on AI, machine learning (ML), and deep learning (DL)—with over 200 breakout sessions, hands-on workshops, deep-dive chalk talks, and more. You’ll hear success stories […]

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Congratulations to the Winners of the re:Invent Robocar Rally 2017!

To drive awareness of deep learning, machine learning, and the internet of things in autonomous driving, AWS hosted a hackathon—the Robocar Rally—at re:Invent in November 2017. We kicked off Robocar Rally in September with a series of blog posts and Twitch streams. At re:Invent, we had 100 attendees come for a hands-on two-day hackathon using deep learning and the open source Donkey Car platform with AWS machine learning services and AWS IoT. They formed teams, and built, customized, trained, and raced their own 1/16th scale cars. There’s a lot we could talk about, but we think this video shows the event better than we could write about it.

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