Devoxx: Building AI-powered applications on AWS
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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
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Devoxx: Building AI-powered applications on AWS
- 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Adrian Hornsby, Technical Evangelist Building AI-powered application on AWS @adhorn
- 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. • Technical Evangelist, Developer Advocate, … Software Engineer • Own bed in Finland • Previously: • Solutions Architect @AWS • Lead Cloud Architect @Dreambroker • Director of Engineering, Software Engineer, DevOps, Manager, ... @Hdm • Researcher @Nokia Research Center • and a bunch of other stuff. • Climber, like Ginger shots. @adhorn
- 3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. What to expect from this session 1. A little bit history & theory never kills 2. AI in AWS 3. Building AI-powered apps x4
- 4. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Event driven A B CEvent on B by A triggers C Invocation Lambda functions Action
- 5. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. How Lambda works S3 event notifications DynamoDB Streams Kinesis events Cognito events SNS events Custom events CloudTrail events LambdaDynamoDB Kinesis S3 Any custom Invoked in response to events - Changes in data - Changes in state Redshift SNS Access any service, including your own Such as… Lambda functions CloudWatch events
- 6. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Event-driven using Lambda AWS Lambda: Resize Images Users upload photos S3: Source Bucket S3: Destination Bucket Triggered on PUTs
- 7. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
- 8. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. No servers to provision or manage Scales with usage Never pay for idle Availability and fault tolerance built in Serverless means…
- 9. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. EVENT DRIVEN CONTINUOUS SCALING PAY BY USAGE Serverless means…
- 10. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. The rise of AI
- 11. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. The Advent of AI & Deep Learning Data GPUs & Acceleration Cloud Computing Algorithms AWS
- 12. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
- 13. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AI On AWS Today
- 14. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AI & Deep Learning in the hands of every developer
- 15. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Voice enabled applications
- 16. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Polly: Text In, Life-like Speech Out Amazon Polly “Today in Seattle, WA it’s 11°F” “Today in Seattle Washington it’s 11 degrees Fahrenheit”
- 17. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. https://www.w3.org/TR/speech-synthesis/ <speak> The spelling of my name is <prosody rate='x-slow'> <say-as interpret-as="characters">Adrian</say-as> </prosody> </speak> A Focus On Voice Quality & Pronunciation
- 18. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. <API> Amazon Polly </API> aws polly synthesize-speech --text "It was nice to live such a wonderful live show" --output-format mp3 --voice-id Joanna --text-type text johanna.mp3
- 19. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
- 20. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. <demo> PollyCast </demo> * Initial project by James Siri, Piotr Lewalski https://github.com/adhorn/pollycast
- 22. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Image analysis
- 23. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Amazon Rekognition Object and scene detection Facial analysis Face comparison Celebrity recognition Image moderation Rekognition: Images In, Rich Metadata Out
- 24. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. <API> Amazon Rekognition </API> aws rekognition detect-faces --image '{"S3Object":{"Bucket":"adhorn-reko","Name":"horse.jpg"}}' --attributes "ALL" aws rekognition detect-labels --image '{"S3Object":{"Bucket":"adhorn-reko","Name":"horse.jpg"}}'
- 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
- 26. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. <demo> Image Recognition and Processing Backend Step Functions </demo> https://github.com/awslabs/lambda-refarch-imagerecognition
- 28. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Backend powered by Step Functions
- 29. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Start Sequential Steps U p l o a d R AW f i l e D e l e t e R AW f i l e End AWS Step Functions
- 30. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. S e l e c t i m a g e c o n v e rt e r RA W t o J P E G RA W t o P NGRA W t o TI FF L o a d i n Da t a b a se Start End Un s u p p or te d i m a g e t yp eParallel Steps AWS Step Functions
- 31. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. P r o c e s s p h o t o Re s i ze i m a g e Start End E xt r a c t m e t a d a ta Fa c i a l r e c o g n it i on L o a d i n Da t a b a se Branching Steps AWS Step Functions
- 32. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AWS Step Functions: Orchestrate a Serverless processing workflow using AWS Lambda
- 33. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. <demo> Poliko powered by Amazon Polly & Rekognition </demo> http://poliko.adhorn.me
- 34. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Demo - Poliko Poliko Take Pic Amazon Cognito 2. Detect Labels 4. Synthesize-speech Amazon Rekognition Amazon Polly 3. Detect Faces Amazon S3 “Static website hosting” enabled
- 35. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Deep Learning enabled applications
- 36. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. One-Click Deep Learning AWS Deep Learning AMIs Amazon Linux & Ubuntu Up to~40k CUDA cores Apache MXNet TensorFlow Theano Keras Caffe CNTK Torch Pre-configured CUDA drivers Anaconda, Python3 Out-of-the-box Tutorials + CloudFormation template + Container Image Available in the AWS Marketplace AI Frameworks on AWS
- 37. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Early detection of diabetic complications
- 38. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Autonomous Driving Systems
- 39. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Pre-trained MXNet models http://data.mxnet.io/models/imagenet/
- 40. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. ResNet – Deep Learning Based on: Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun https://arxiv.org/pdf/1512.03385.pdf https://medium.com/towards-data-science/neural-network-architectures-156e5bad51ba
- 41. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Scale Predictions with AWS Lambda and MXNet AWS LambdaAmazon API Gateway Amazon S3 Training Inference https://aws.amazon.com/blogs/compute/seamlessly-scale-predictions-with-aws-lambda-and-mxnet/
- 42. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Predict with AWS Lambda
- 43. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Predict with AWS Lambda
- 44. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Predict with AWS Lambda
- 45. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Predict with AWS Lambda
- 46. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. AI for everyone!
- 47. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Thanks you!
Editor's Notes
- 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. “ 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. “
- 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.