AWS Machine Learning Blog

Category: Artificial Intelligence

PyTorch 1.0 preview now available in Amazon SageMaker and the AWS Deep Learning AMIs

Amazon SageMaker and the AWS Deep Learning AMIs (DLAMI) now provide an easy way to evaluate the PyTorch 1.0 preview release. PyTorch 1.0 adds seamless research-to-production capabilities, while retaining the ease-of-use that has enabled PyTorch to rapidly gain popularity. The AWS Deep Learning AMI comes pre-built with PyTorch 1.0, Anaconda, and Python packages, with CUDA and […]

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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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Deploy your own TensorFlow object detection model to AWS DeepLens

In this blog post, we’ll show you how to deploy a TensorFlow object detection model to AWS DeepLens. This enables AWS DeepLens to perform real-time object detection using the built-in camera. Object detection is the technique for machines to correctly identify different objects in the image or video. Image recognition, specifically object detection is a […]

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Segmenting brain tissue using Apache MXNet with Amazon SageMaker and AWS Greengrass ML Inference – Part 1

Annotation and segmentation of medical images is a laborious endeavor that can be automated in part via deep learning (DL) techniques. These approaches have achieved state-of-the-art results in generic segmentation tasks, the goal of which is to classify images at the pixel level. In Part 1 of this blog post, we demonstrate how to train […]

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Help improve lives through Machine Learning by joining the AWS DeepLens Challenge!

Today, we’re unveiling a fresh approach to the AWS DeepLens Challenge. We are bringing you four challenges to choose from–sustainability, games, health and inclusivity. Now you can be inspired to create machine learning projects with AWS DeepLens and make a difference at the same time! Use these challenges to gain machine learning experience with fun, […]

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Amazon SageMaker automatic model tuning produces better models, faster

Amazon SageMaker recently released a feature that allows you to automatically tune the hyperparameter values of your machine learning model to produce more accurate predictions. Hyperparameters are user-defined settings that dictate how an algorithm should behave during training. Examples include how large a decision tree should be grown, the number of clusters desired from a […]

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Discovering and indexing podcast episodes using Amazon Transcribe and Amazon Comprehend 

As an avid podcast listener, I had always wished for an easy way to glimpse at the transcript of an episode to decide whether I should add it to my playlist (not all episode abstracts are equally helpful!). Another challenge with podcasts is that, although they contain a wealth of knowledge that is often not […]

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New Engen improves customer acquisition marketing campaigns using Amazon Rekognition

New Engen is a cross-channel performance marketing technology company that uses its proprietary software products and creative solutions to help their clients acquire new customers. New Engen integrates marketing, AI, and creative expertise to provide a one-stop solution that helps their customers optimize their digital marketing budget across Facebook, Google, Instagram, Snap, and more. Improving […]

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Save time and money by filtering faces during indexing with Amazon Rekognition

Amazon Rekognition is a deep-learning-based image and video analysis service that can identify objects, people, text, scenes, and activities, as well as detect any inappropriate content. Using the new Amazon Rekognition face filtering feature, you can now have control over the quality and quantity of faces you can index for face recognition. This saves on […]

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Training models with unequal economic error costs using Amazon SageMaker

Many companies are turning to machine learning (ML) to improve customer and business outcomes. They use the power of ML models built over “big data” to identify patterns and find correlations. Then they can identify appropriate approaches or predict likely outcomes based on data about new instances. However, as ML models are approximations of the […]

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