AWS Machine Learning Blog

Category: Artificial Intelligence

Build smart chat apps with Amazon machine learning APIs and the PubNub ChatEngine

The demand for chat apps is growing wildly. From social apps to business collaboration, in-game chat to customer support, every app needs chat. Chat apps like Telegram are making mainstream news and generating widespread interest.

Historically, building a scalable, feature-rich chat app was surprisingly difficult. Not anymore. PubNub ChatEngine has integrated Amazon machine learning APIs, which makes building intelligent, scalable chat apps super easy for mobile and web.

In this blog post, you’ll learn how to spin up the PubNub ChatEngine and add Amazon Translate and Amazon Polly for cross-lingual, speech-enabled chat apps that you can embed anywhere.

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Create accessible training with Initiafy and Amazon Polly

We’ve become so accustomed to the existence of skyscrapers, road networks, oil rigs, hydroelectric dams, nuclear power plants, wind turbines, suspension bridges, and all the other incredible feats of human engineering populating our world, that some might overlook the remarkable effort that goes into their construction. These projects are not built by robots. A huge amount of human effort is required to bring them into existence, in addition to the technology used to design, streamline, and improve processes.

Initiafy makes the workforce element of projects more productive and efficient. Heavy industry projects rely on contractors, with related management challenges of handling documents, health and safety, and quality control. Initiafy provides contractor management on an online platform, giving workers training in a flexible way and ensuring standards are high across a contingent workforce. Initiafy operates in a number of countries, with workers coming from a number of nationalities so it is vital that training is accessible for all workers.

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Learn about SafeHaven: The third place winner of the AWS DeepLens Challenge Hackathon

Nathan Stone (NS) and Peter McLean (PM) are a team both professionally at Haven Power, a business energy supplier in Ipswich, UK, and also off the clock when they recently collaborated to create SafeHaven, the third place winner in the AWS DeepLens Challenge. SafeHaven was designed to protect vulnerable people, by enabling them to identify […]

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Text Classification with Gluon on Amazon SageMaker and AWS Batch

Our customer had a problem: The manual classification of warranty claims was causing a bottleneck. These claims were based on a text field that explained the event in short detail. An example of that text looked something like this: “The plutonium-fueled nuclear reactor overheated on a hot day in Arizona’s recent inclement weather. Burn damage […]

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Train faster, more flexible models with Amazon SageMaker Linear Learner

Today Amazon SageMaker is launching several additional features to the built-in linear learner algorithm. Amazon SageMaker algorithms are designed to scale effortlessly to massive datasets and take advantage of the latest hardware optimizations for unparalleled speed. The Amazon SageMaker linear learner algorithm encompasses both linear regression and binary classification algorithms. These algorithms are used extensively in […]

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Faster training with optimized TensorFlow 1.6 on Amazon EC2 C5 and P3 instances

The AWS Deep Learning AMIs come with latest pip packages of popular deep learning frameworks pre-installed in separate virtual environments so that developers can quickly get started with training deep learning models. The new version of the Deep Learning AMIs for Ubuntu and Amazon Linux now come with TensorFlow 1.6, built with advanced optimizations for […]

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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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