AI on AWS : DevDays India
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AWS Devdays in New Delhi, Pune, Hyderabad sessions on AI Services on AWS.
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AI on AWS : DevDays India
- 2. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Twitter: @madhushekar23 LinkedIn: /in/madhusudanshekar AI on AWS Madhusudan Shekar
- 3. Serverless At the Edge, On IoT Devices Algorithms Tons of CPUs Elastic capacity Software Lowering Cost on Data Storage Data PBs of existing data Algorithms and Neural Networks
- 4. Amazon AI Intelligent Services Powered By Deep Learning
- 5. Amazon AI Services Polly Text-to-Speech Rekognition Lex Image Analysis ASR & NLU
- 6. Amazon AI Services Polly Text-to-Speech Rekognition Lex Image Analysis ASR & NLU
- 7. Amazon Polly: Life-like Speech Service Converts text to life-like speech 50 voices 24 languages Low latency, real time Fully managed
- 8. Amazon Polly: Language Portfolio Americas: • Brazilian Portuguese • Canadian French • English (US) • Spanish (US) A-PAC: • Australian English • Indian English • Japanese EMEA: • British English • Danish • Dutch • French • German • Icelandic • Italian • Norwegian • Polish • Portuguese • Romanian • Russian • Spanish • Swedish • Turkish • Welsh • Welsh English
- 9. Let’s take a listen…
- 10. Amazon Polly: Text In, Life-like Speech Out Amazon Polly “The temperature in WA is 75°F” “The temperature in Washington is 75 degrees Fahrenheit”
- 11. “Today in Mumbai, India, it’s 31°C” ‘"We live for the music" live from the Madison Square Garden.’ 1. Automatic, Accurate Text Processing Polly: A Focus On Voice Quality & Pronunciation
- 12. Polly: A Focus On Voice Quality & Pronunciation 2. Intelligible and Easy to Understand 1. Automatic, Accurate Text Processing
- 13. 2. Intelligible and Easy to Understand 3. Add Semantic Meaning to Text “Richard’s number is 2122341237“ “Richard’s number is 2122341237“ Telephone Number Polly: A Focus On Voice Quality & Pronunciation 1. Automatic, Accurate Text Processing
- 14. Polly: A Focus On Voice Quality & Pronunciation 2. Intelligible and Easy to Understand 1. Automatic, Accurate Text Processing 3. Add Semantic Meaning to Text 4. Speech Effect: Wisper
- 15. TEXT Market grew by > 20%. WORDSPHONEMES { { { { { ˈtwɛn.ti pɚ.ˈsɛnt ˈmɑɹ.kət ˈgɹu baɪ ˈmoʊɹ ˈðæn PROSODY CONTOURUNIT SELECTION AND ADAPTATION TEXT PROCESSING PROSODY MODIFICATIONSTREAMING Market grew by more than twenty percent Speech units inventory
- 17. GoAnimate is a cloud-based, animated video creation plarform. Amazon Polly gives GoAnimate users the ability to immediately give voice to the characters they animate using our platform. Alvin Hung CEO, GoAnimate ” “ • Multi-language communication • Training or HR professionals who have to create content in many languages • Video preproduction • Video makers who need to iterate and fine-tune before the text-to- speech is eventually replaced by a professional voiceover • K–12 education • Students who make videos and don’t have access to professional voices or time for or knowledge of voiceover With Polly, GoAnimate gives voice to the characters in their animations
- 18. Royal National Institute of Blind People creates and distributes accessible information in the form of synthesized content Amazon Polly delivers incredibly lifelike voices which captivate and engage our readers. John Worsfold Solutions Implementation Manager, RNIB ” “ • RNIB delivers largest library of audiobooks in the UK for nearly 2 million people with sight loss • Naturalness of generated speech is critical to captivate and engage readers • No restrictions on speech redistributions enables RNIB to create and distribute accessible information in a form of synthesized content RNIB provides the largest library in the UK for people with sight loss
- 19. Amazon AI Services Polly Text-to-Speech Rekognition Lex Image Analysis ASR & NLU
- 20. Rekognition: Search & Understand Visual Content Real-time & batch image analysis Object & Scene Detection Face Comparison Face SearchFacial Analysis
- 21. Deer 98.8% Wildlife 95.1% Conifer 95.1% Spruce 95.1% Wood 78.3% Tree 63.5% Forest 63.5% Vegetation 61.9% Pine 60.6% Outdoors 54.0% Flower 53.9% Plant 52.9% Nature 50.7% Field 50.7% Grass 50.7%
- 22. Age Range 38-59 Beard: False 84.3% Emotion: Happy 86.5% Eyeglasses: False 99.6% Eyes Open: True 99.9% Gender: Male 99.9% Mouth Open: False 86.2% Mustache: False 98.4% Smile: True 95.9% Sunglasses: False 99.8% Bounding Box Height: 0.36716.. Left: 0.40222.. Top: 0.23582.. Width: 0.27222.. Landmarks EyeLeft EyeRight Nose MouthLeft MouthRight LeftPupil RightPupil LeftEyeBrowLeft LeftEyeBrowRight LeftEyeBrowUp : Quality Brightness 52.5% Sharpness 99.9%
- 23. Collection IndexFaces SearchFacesbyImage Nearest neighbor search FaceID: 4c55926e-69b3-5c80-8c9b-78ea01d30690 Similarity: 97 FaceID: 02e56305-1579-5b39-ba57-9afb0fd8782d Similarity: 92 FaceID: 02e56305-1579-5b39-ba57-9afb0fd8782d Similarity: 85
- 24. Image Moderation Explicit Nudity Nudity Graphic Male Nudity Graphic Female Nudity Sexual Activity Partial Nudity Suggestive Female Swimwear or Underwear Male Swimwear or Underwear Revealing Clothes
- 25. "Id": "1SK7cR8M", "MatchConfidence": 100, "Name": "Jeff Bezos", "Urls": [ "www.imdb.com/name/nm1757263 " ] Celebrity Recognition
- 26. Lets Explore Rekognition : Demo
- 27. 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
- 28. Amazon AI Services Polly Text-to-Speech Rekognition Lex Image Analysis ASR & NLU
- 29. The Advent Of Conversational Interactions 1st Gen: Machine-oriented interactions
- 30. The Advent Of Conversational Interactions 2nd Gen: Control-oriented & translated 1st Gen: Machine-oriented interactions
- 31. The Advent Of Conversational Interactions 1st Gen: Machine-oriented interactions 2nd Gen: Control-oriented & translated 3rd Gen: Intent-oriented
- 32. Lex: Build Natural, Conversational Interactions In Voice & Text Voice & Text “Chatbots” Powers Alexa Voice interactions on mobile, web & devices Text interaction with Slack & Messenger Enterprise Connectors (with more coming) Salesforce Microsoft Dynamics Marketo Zendesk Quickbooks Hubspot
- 34. Lets order some coffee : Demo
- 35. Customer Testimonials: HubSpot “Through Amazon's Lex, we're adding sophisticated natural language processing capabilities that helps GrowthBot provide a more intuitive UI for our users. Amazon Lex lets us take advantage of advanced A.I. and machine learning without having to code the algorithms ourselves.” “HubSpot's GrowthBot is an all-in-one chatbot which helps marketers and sales people be more productive by providing access to relevant data and services using a conversational interface. With GrowthBot, marketers can get help creating content, researching competitors, and monitoring their analytics.”
- 36. Customer Testimonials: Capital One “A highly scalable solution, it also offers potential to speed time to market for a new generation of voice and text interactions such as our recently launched Capital One skill for Alexa.” “As a heavy user of AWS, Amazon Lex’s seamless integration with other AWS services like AWS Lambda and AWS DynamoDB is really appealing.”
- 37. Elastic GPUs On EC2 P2M4 D2 X1 G2T2 R4 I3 C5 General Purpose GPU General Purpose Dense storage Large memory Graphics intensive Memory intensive High I/O Compute intensiveBurstable Lightsail Simple VPS F1 FPGAs Instance Families
- 38. Up to 40 thousand parallel processing cores 70 teraflops (single precision) over 23 teraflops (double precision) Instance Size GPUs GPU Peer to Peer vCPUs Memory (GiB) Network Bandwidth* p2.xlarge 1 - 4 61 1.25Gbps p2.8xlarge 8 Y 32 488 10Gbps p2.16xlarge 16 Y 64 732 20Gbps *In a placement group Amazon EC2 P2 Instances
- 39. F1 Instances: Bringing Hardware Acceleration To All FPGA Images Available In AWS Marketplace F1 Instance W ith your custom logic running on an FPGA Develop, simulate, debug & compile your code Package as FPGA Images
- 40. Deep Learning Frameworks MXNet, Caffe, Tensorflow, Theano, and Torch Pre-installed components to speed productivity, such as Nvidia drivers, cuDNN, Anaconda, Python 2 & 3 AWS Integration Deep Learning AMI
- 41. Deep Learning Framework Comparison Apache MXNet TensorFlow Cognitive Toolkit Industry Owner N/A – Apache Community Google Microsoft Programmability Imperative and Declarative Declarative only Declarative only Language Support R, Python, Scala, Julia, Cpp. Javascript, Go, Matlab and more.. Python, Cpp. Experimental Go and Java Python, Cpp, Brainscript. Code Length| AlexNet (Python) 44 sloc 107 sloc using TF.Slim 214 sloc Memory Footprint (LSTM) 2.6GB 7.2GB N/A
- 42. DEMO Realtime detection and tracking on TX1 ~10 frame/sec with 640x480 resolutionAutonomous driving at night
- 43. Some Quick Links on Deep Learning Recommendation Engine: https://github.com/amzn/amazon-dsstne It’s a sparse tensor network, suited for recommendations. MXNet Learning: https://becominghuman.ai/an-introduction-to-the- mxnet-api-part-1-848febdcf8ab This is a 6-part series that walks through learning MXNet.
- 44. High quality, through best-in-class deep learning Deep functionality Easy to use & thoughtfully integrated Built for production Low cost AWS AI Services
- 45. Thank You! Twitter: @madhushekar23 LinkedIn: /in/madhusudanshekar
- 46. Don’t Forget Feedback Forms!
Editor's Notes
- The basics are pretty simple, but the service has deep functionality. You can send the service a simple it 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, like I just had to. 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.
- 19 min. DEEP LEARNING FOR G2P and PROSODY CONTOUR For phonemes – about use of Machine Learning For Contour – again – LSTM: We took audio Mention units adaptation to make sure that units match eacg other
- STORY BACKGROUND SOLUTION AND BENEFITS ADDITIONAL INFORMATION
- STORY BACKGROUND SOLUTION AND BENEFITS ADDITIONAL INFORMATION
- It’s clear we love us some compute Workloads are not vanilla - they are of different sizes and constraints Like building a house: you don’t use just one tool, you use lots of tools in your toolbox. It’s also true with any of these building block services; the right tool for the job you need to get done.
- You can also add graphics processing which looks and operates just like a GPU. You can use the same OpenGL code that your application or game already uses, and have them rendered on a GPU. This is perfect if you only need a small part of a GPU more cost effectively (with the smallest option starting at just 1/8th of a GPU), or would like to be able to add graphics processing capabilities to instances which are optimized for I/O, storage, or memory workloads (scaling all the way up to connecting one or more full GPUs).
- 1/Develop, simulate, debug & compile your code 2/HW development kit and FPGA image 3/Create your own FPGA acceleration that you package into FPGA image 4/ Upload FPGA image 5/ MP
- References: https://github.com/tensorflow/models/blob/master/slim/nets/alexnet.py https://github.com/dmlc/mxnet/blob/master/example/image-classification/symbols/alexnet.py https://github.com/Microsoft/CNTK/blob/master/Examples/Image/Classification/AlexNet/Python/AlexNet_ImageNet_Distributed.py https://arxiv.org/pdf/1608.00895.pdf