Slides from my talk at devoxx2018
The video: https://www.youtube.com/watch?v=-izfBVlHkSc
https://cfp.devoxx.be/2017/talk/XEO-9942/Building_Serverless_AI-powered_Applications_on_AWS
25. Amazon Rekognition
Customers
• Digital Asset Management
• Media and Entertainment
• Travel and Hospitality
• Influencer Marketing
• Systems Integration
• Digital Advertising
• Consumer Storage
• Law Enforcement
• Public Safety
• eCommerce
• Education
you have a lot to cover and you are happy to field questions after the talk.
And the result of this is that we see a ton of machine learning up on AWS today, literally from A through to Z. So everything from Ancestry, who are using machine learning and deep learning to be able to process genomic information and build out family trees, all the way through to Zillow, who use machine learning to do house-price estimation up on the website.
The basics are pretty simple, but the service has deep functionality.
You can send the service a simple string of text, and it will generate the life like voice in your choice of 47 different voices.
But it’s not naive of the context of the text. For example, the text here - ‘WA’ and ‘degree F’, that would sound strange if it were spoken out loud.
Instead, Polly will automatically expand the text strings ‘WA’ and ‘degree F’, to ‘Washington’ and ‘degrees fahrenheit’, to create more life like speech. The developer doesn’t have to do anything - just send the text, and get life like voice back.
Speech Synthesis Markup Language (SSML) Version 1.0
The Voice Browser Working Group has sought to develop standards to enable access to the Web using spoken interaction.
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Amazon Rekognition currently supports the JPEG and PNG image formats. You can submit images either as an S3 object or as a byte array.Amazon Rekognition supports image file sizes up to 15MB when passed as an S3 object, and up to 5MB when submitted as an image byte array.Amazon Rekognition is currently available in US East (Northern Virginia), US West (Oregon) and EU (Ireland) regions.
Mxnet convolutional deep neural networks (CNNs),
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On click – Marketplace
AMI supported and maintained by Amazon Web Services for use on EC2.
Designed to provide a stable, secure, and high performance execution environment for deep learning applications running on Amazon EC2.
Popular deep learning frameworks, including MXNet, Caffe, Tensorflow, Theano, CNTK and Torch
as Packages that enable easy integration with AWS, including launch configuration tools and many popular AWS libraries and tools.
It also includes the Anaconda Data Science Platform for Python2 and Python3.
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The AWS Deep Learning AMIs equip data scientists, machine learning practitioners, and research scientists with the infrastructure and tools to accelerate work in deep learning, in the cloud, at any scale. You can quickly launch Amazon EC2 instances on Amazon Linux or Ubuntu, pre-installed with popular deep learning frameworks to train sophisticated, custom AI models, experiment with new algorithms, or to learn new skills and techniques. The Deep Learning AMIs let you create managed, auto-scaling clusters of GPUs for large scale training, or run inference on trained models with compute-optimized or general purpose CPU instances, using Apache MXNet, TensorFlow, the Microsoft Cognitive Toolkit (CNTK), Caffe, Caffe2, Theano, Torch and Keras.
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So diabetic blindness is the leading cause of blindness in men in the U.S. between the ages of 21 and 46, and it's preventable in almost all cases if you can catch it early enough. The challenge is that the only way to catch it is to look at images like this. This is a fundoscope. You're looking for very, very small changes in the blood vessels at the back of the eye, which usually requires a human to look at and review, a highly trained human whose maybe use and skills are better served elsewhere.
So we trained a deep-learning model. We took pictures of healthy eyes and unhealthy eyes and trained a deep-learning model that was able to predict diabetic complications, which went on to prevention in 90 percent of cases.
ResNet have a simple ideas: feed the output of two successive convolutional layer AND also bypass the input to the next layers! This is also the very first time that a network of > hundred, even 1000 layers was trained.
We compiled and built the MXNet libraries to demonstrate how Lambda scales the prediction pipeline to provide this ease and flexibility for machine learning or deep learning model prediction. We built a sample application that predicts image labels using an 18-layer deep residual network. The model architecture is based on the winning model in the ImageNet competition called ResidualNet. The application produces state-of-the-art results for problems like image classification.