AWS Machine Learning Blog

Detect sentiment from customer reviews using Amazon Comprehend

In today’s world, public content has never been more relevant. Data from customer reviews is being used as a tool to gain insight into consumption-related decisions as the understanding of its associated sentiment grants businesses invaluable market awareness and the ability to proactively address issues early. Sentiment analysis uses a process to computationally determine whether […]

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AWS Deep Learning AMIs now come with TensorFlow 1.5 and new Model Serving capabilities

The AWS Deep Learning AMIs help you quickly and easily get started with machine learning. The AMIs include a range of prebuilt options that cater to the diverse needs of machine learning practitioners. For those who want the latest stock versions of deep learning frameworks, the Deep Learning AMIs provide prebuilt pip binaries installed in […]

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Dive Deep into AWS DeepLens Lambda Functions and the New Model Optimizer

by Jyothi Nookula and Eddie Calleja | on | in AWS DeepLens | Permalink | Comments |  Share

Today we launched a new Model Optimizer for AWS DeepLens, which will optimize your deep learning models to run on the DeepLens GPU efficiently, with a single line of Python. The Model Optimizer is available in AWS DeepLens software version 1.2.0. To access the GPU for inference, AWS DeepLens uses the Cl-DNN, Compute Library for […]

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Speeding up Apache MXNet using the NNPACK library

Apache MXNet is an open source library developers can use to build, train, and re-use deep learning networks. In this  blog post, I’ll show you to speed up inference by using the NNPACK library. Indeed, when GPU inference is not available, adding NNPACK to Apache MXNet might be a simple option to extract more performance […]

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Amazon SageMaker BlazingText: Parallelizing Word2Vec on Multiple CPUs or GPUs

by Saurabh Gupta and Vineet Khare | on | in SageMaker | Permalink | Comments |  Share

Today we’re launching Amazon SageMaker BlazingText as the latest built-in algorithm for Amazon SageMaker. BlazingText is an unsupervised learning algorithm for generating Word2Vec embeddings. These are dense vector representations of words in large corpora. We’re excited to make BlazingText, the fastest implementation of Word2Vec, available to Amazon SageMaker users on: Single CPU instances (like the […]

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Build a social media dashboard using machine learning and BI services

In this blog post we’ll show you how you can use Amazon Translate, Amazon Comprehend, Amazon Kinesis, Amazon Athena, and Amazon QuickSight to build a natural-language-processing (NLP)-powered social media dashboard for tweets. Social media interactions between organizations and customers deepen brand awareness. These conversations are a low-cost way to acquire leads, improve website traffic, develop […]

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AWS KMS-based Encryption Is Now Available for Training and Hosting in Amazon SageMaker

by Kumar Venkateswar | on | in SageMaker | Permalink | Comments |  Share

Amazon SageMaker uses throwaway keys, also called transient keys, to encrypt the ML General Purpose storage volumes attached to training and hosting EC2 instances. Because these keys are used only to encrypt the ML storage volumes and are then immediately discarded, the volumes can safely be used to store confidential data. Volumes can be accessed […]

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Build an Amazon Lex Chatbot with Microsoft Excel

This is a guest post by AWS Community Hero Cyrus Wong. Our institution (IVE) here in Hong Kong has begun experimenting with Amazon Lex in teaching, research, and healthcare. We have many non-technical employees, such as English teachers in IVE and therapists from IVE Childcare, Elderly and Community Services Discipline, who don’t have the technical […]

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Making neural nets uncool again – AWS style

by Jeremy Howard and Joseph Spisak | on | in SageMaker | Permalink | Comments |  Share

Just as the goal of Amazon AI is to democratize machine learning with the development of platforms such as Amazon SageMaker, the goal of fast.ai is to level the educational playing field so that anyone can pick up machine learning and be productive. The fast.ai tagline is “Making neural nets uncool again.” This is not a play to decrease the popularity of deep neural networks, but instead to broaden their appeal and accessibility beyond the academic elites who have dominated the research in this area.

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AWS CloudTrail integration is now available in Amazon SageMaker

AWS customers have been requesting a way to log activity in Amazon SageMaker, to help you meet your governance and compliance needs. I’m happy to announce that Amazon SageMaker is now integrated with AWS CloudTrail, a service that enables you to log, continuously monitor, and retain account information related to Amazon SageMaker API activity. Amazon […]

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