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Introduction to Artificial Intelligence and Machine Learning services at AWS - AWS Summit Cape Town 2017

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AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and text-to-speech (TTS) with Amazon Polly, visual search and image recognition with Amazon Rekognition, and developer-focused machine learning with Amazon Machine Learning. In this talk you will learn about these services and see demos of their capabilities

AWS Speaker: Denis V. Batalov, Solutions Architect - Amazon Web Services
Customer Speaker: Tom Wells - Synthesis Software Technologies

Published in: Technology
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Introduction to Artificial Intelligence and Machine Learning services at AWS - AWS Summit Cape Town 2017

  1. 1. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Denis V. Batalov, PhD AWS Solutions Architect, EMEA July 5th, 2017 Introduction to Amazon AI services @dbatalov
  2. 2. Artificial Intelligence At Amazon (1995)
  3. 3. Thousands Of Employees Across The Company Focused on AI Discovery & Search Fulfilment & Logistics Enhance Existing Products Define New Product Categories Bring Machine Learning To All Artificial Intelligence At Amazon (2017)
  4. 4. Automatic Produce Quality Detection 7/25/17 6
  5. 5. AI Applications on AWS Pinterest Lens Netflix Recommendation Engine
  6. 6. AI Applications on AWS Zillow • Zestimate (using Apache Spark) Howard Hughes Corp • Lead scoring for luxury real estate purchase predictions FINRA • Anomaly detection, sequence matching, regression analysis, network/tribe analysis Netflix • Recommendation engine Pinterest • Image recognition search Fraud.net • Detect online payment fraud DataXu • Leverage automated & unattended ML at large scale (Amazon EMR + Spark) Mapillary • Computer vision for crowd sourced maps Hudl • Predictive analytics on sports plays Upserve • Restaurant table mgmt & POS for forecasting customer traffic TuSimple • Computer Vision for Autonomous Driving Clarifai • Computer Vision APIs
  7. 7. AI Services AI Platform Amazon Rekognition Amazon Polly Amazon Lex More to come in 2017 Amazon Machine Learning Amazon Elastic MapReduce Spark & SparkML More to come in 2017 Amazon AI: Democratized Artificial Intelligence AI Engines Apache MXNet Caffe Theano KerasTorch CNTKTensorFlow P2 ECS Lambda GreenGrass FPGAEMR/Spark More to come in 2017 Hardware
  8. 8. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Tom Wells - Synthesis Software Technologies 5th July 2017 How to fail at automated bitcoin trading
  9. 9. Tom Wells Chief Disruption Officer
  10. 10. Awesome team
  11. 11. Awesome customers
  12. 12. Awesome technology #aws #blockchain #ai#cryptography #ml #bigdata #distributed #containers #iac #digital_channels
  13. 13. © 2015, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Tom Wells - Synthesis Software Technologies 5th July 2017 How to fail at automated bitcoin trading (quickly & cheaply)
  14. 14. ROI cost revenue Unlocking innovation Culture Process
  15. 15. “How many successful innovation projects have you run, and how much additional revenue did they make?” à “How many failed innovation projects have you run, and how much did they cost?”
  16. 16. bought 2 BTC “Invest!”
  17. 17. bought 2 BTC #ftw!!
  18. 18. API ECS Cluster t2.micro t2.micro Containers luno-trade-feed (service) DynamoDB - luno-trades WS://
  19. 19. API ECS Cluster t2.micro t2.micro Containers luno-trade-feed (service) DynamoDB luno-trades extract-to-s3 (once-off task) S3 luno-trades Extract Store CloudWatch Event Midnight UTC Trigger Lambda Run ECS Task Run Task WS://
  20. 20. Not a data scientist Not a trader
  21. 21. API ECS Cluster t2.micro t2.micro Containers luno-trade-feed (service) DynamoDB luno-trades extract-to-s3 (once-off task) S3 luno-trades Extract Store CloudWatch Event Midnight UTC Trigger Lambda Run ECS Task Run Task WS:// European Central Bank Website (.xml) Lambda Extract ECB Rates Trigger Store Lambda Extract BTC/USD Prices blockchain.info (.csv) Scrape Store Trigger Scrape DynamoDB currency-rates DynamoDB btc-usd-trades Extract Extract
  22. 22. API ECS Cluster t2.micro t2.micro Containers luno-trade-feed (service) DynamoDB luno-trades extract-to-s3 (once-off task) S3 luno-trades Extract Store CloudWatch Event Midnight UTC Trigger Lambda Run ECS Task Run Task WS:// European Central Bank Website (.xml) Lambda Extract ECB Rates Trigger Store Lambda Extract BTC/USD Prices blockchain.info (.csv) Scrape Store Trigger Scrape DynamoDB currency-rates DynamoDB btc-usd-trades Extract Extract bitfinex-trade-feed (service) API WS:// Store
  23. 23. Hypothesis #1: Predict “normalized” ZAR price based on ZAR price x minutes ago
  24. 24. Hypothesis #2: Predict ZAR price based on USD price
  25. 25. API ECS Cluster t2.micro t2.micro Containers luno-trade-feed (service) DynamoDB luno-trades extract-to-s3 (once-off task) S3 luno-trades Extract Store CloudWatch Event Midnight UTC Trigger Lambda Run ECS Task Run Task WS:// European Central Bank Website (.xml) Lambda Extract ECB Rates Trigger Store Lambda Extract BTC/USD Prices blockchain.info (.csv) Scrape Store Trigger Scrape DynamoDB currency-rates DynamoDB btc-usd-trades Extract Extract bitfinex-trade-feed (service) API WS:// Store EMR Cluster (Spot Instances) c4.xlarge c4.xlarge Jobs fancy predictive stuff …
  26. 26. Lots of code (that doesn’t work because I’m too dumb)
  27. 27. API ECS Cluster t2.micro t2.micro Containers luno-trade-feed (service) DynamoDB luno-trades extract-to-s3 (once-off task) S3 luno-trades Extract Store CloudWatch Event Midnight UTC Trigger Lambda Run ECS Task Run Task WS:// European Central Bank Website (.xml) Lambda Extract ECB Rates Trigger Store Lambda Extract BTC/USD Prices blockchain.info (.csv) Scrape Store Trigger Scrape DynamoDB currency-rates DynamoDB btc-usd-trades Extract Extract bitfinex-trade-feed (service) API WS:// Store EMR Cluster (Spot Instances) c4.xlarge c4.xlarge Jobs fancy predictive stuff …
  28. 28. API ECS Cluster t2.micro t2.micro Containers luno-trade-feed (service) extract-to-s3 (once-off task) S3 luno-trades Extract Store CloudWatch Event Midnight UTC Trigger Lambda Run ECS Task Run Task WS:// European Central Bank Website (.xml) Lambda Extract ECB Rates Trigger Store Lambda Extract BTC/USD Prices blockchain.info (.csv) Scrape Store Trigger Scrape DynamoDB currency-rates DynamoDB btc-usd-trades Extract Extract bitfinex-trade-feed (service) API WS:// Store tradebot-prediction (service) Lambda Dynamo Stream to Kinesis Kinesis Stream luno-trades Kinesis Stream currency-rates Kinesis Stream btc-usd-trades New Item New Item New Item Put Put Put Get Records DynamoDB predicIonsStore Extract DynamoDB luno-trades
  29. 29. What did we learn Ability to predict BTC ZAR price based on USD seems possible..? Ability to quickly dump and store data for later analysis hugely valuable Spark not so great for time-series data (?) Future plans incl. AWS ML, build actual trade-execution, build lots more ML models and race them against each other
  30. 30. Thanks!
  31. 31. Amazon Machine Learning – managed service
  32. 32. Predicting Customer Churn with Amazon ML • https://aws.amazon.com/blogs/ai/predicting-customer- churn-with-amazon-machine-learning/ • https://www.youtube.com/watch?v=D04dxTiDO3E
  33. 33. Amazon AI: New Deep Learning Services Life-like Speech Polly Lex Conversational Engine Rekognition Image Analysis Deep Learning Frameworks MXNet, TensorFlow, Theano, Caffe, Torch
  34. 34. DIY Deep Learning for Custom Models AI Enabled Managed API Services Amazon AI: New Deep Learning Services Polly LexRekognition Deep Learning Frameworks MXNet, TensorFlow, Theano, Caffe, Torch CONTROL USABILITY& SIMPLICITY
  35. 35. Amazon AI Services • Leveraging Amazon internal experiences with AI / ML • Managed API services with embedded AI for maximum accessibility and simplicity • Full stack of platforms and engines for specialized deep learning applications
  36. 36. Converts text to life-like speech 47 voices 24 languages Low latency, real time Fully managed Polly: Life-like Speech Service Voice Quality & Pronunciation 1. Automatic, Accurate Text Processing 2. Intelligible and Easy to Understand 3. Add Semantic Meaning to Text 4. Customized Pronunciation Articles and Blogs Training Material Chatbots (Lex) Public Announcements
  37. 37. Amazon Polly: Quality Natural sounding speech A subjective measure of how close TTS output is to human speech. Accurate text processing Ability of the system to interpret common text formats such as abbreviations, numerical sequences, homographs etc. Today in Las Vegas, NV it's 54°F. "We live for the music", live from the Madison Square Garden. Highly intelligibile A measure of how comprehensible speech is. ”Peter Piper picked a peck of pickled peppers.”
  38. 38. 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
  39. 39. Recording Data for TTS Tons of text Recording script: Few weeks of recordings Automatic selection of texts Recording script: • Covers all combinations of diphones and significant features in a language
  40. 40. an error occurred while searching for your route because snaps weren't all so obedient anymore, now we say apple again. and we say apple, general electric soars today. information on general electric quick breads, zucchini, holiday, crock pot, cake, so are you still keeping tabs on your old team, that weighs more than four tons, disrupts the herring's swim … An apple a day, keeps …
  41. 41. Duolingo voices its language learning service Using Polly Duolingo is a free language learning service where users help translate the web and rate translations. With Amazon Polly our users benefit from the most lifelike Text-to-Speech voices available on the market. Severin Hacker CTO, Duolingo ” “ • Spoken language crucial for language learning • Accurate pronunciation matters • Faster iteration thanks to TTS • As good as natural human speech
  42. 42. Amazon Rekognition Deep learning-based image recognition service Search, verify, and organize millions of images Object and Scene Detection Facial Analysis Facial Recognition Image Moderation Integrated with S3, Lambda, Polly, Lex
  43. 43. Object and Scene Detection Generate labels for thousands of objects, scenes, and concepts, each with a confidence score • Search, filter, and curate image libraries • Smart searches for user generated content • Photo, travel, real estate, vacation rental applications Maple Plant Villa Garden Water Swimming Pool Tree Potted Plant Backyard
  44. 44. Facial Analysis Locate faces within images and analyze face attributes to detect emotion, pose, facial landmarks, and features • Avoid faces when cropping images and overlaying ads • Capture user demographics and sentiment • Recommend the best photos • Improve online dating match recommendations • Dynamic, personalized ads
  45. 45. Face Comparison Measure the likelihood that faces in two images are of the same person • Add face verification to applications and devices • Extend physical security controls • Provide guest access to VIP-only facilities • Verify users for online exams and polls
  46. 46. Facial Recognition Identify people in images by finding the closest match for an input face image against a collection of stored face vectors • Add friend tagging to social and messaging apps • Assist public safety officers find missing persons • Identify employees as they access sensitive locations • Identify celebrities in historical media archives
  47. 47. Media Case Study Identify who is on camera at what time for each of 8 networks so that recorded video streams can be indexed and searched Video frame-sampling facial recognition solution using Amazon Rekognition: • Indexed 97,000 people into a face collection in 1 day • Sample frames every 6 secs and test for image variance • Upload images to S3 and call Rekognition to find best facial match • Store time stamp and faceID metadata
  48. 48. I see… Amazon Rekognition Amazon Polly Camera Raspberry Pi Voice Synthesize Speech Detect Labels Detect Faces
  49. 49. Deep Learning
  50. 50. Algorithms Data Programming Models GPUs & Acceleration The Advent of Deep Learning image understanding natural language processing speech recognition autonomy
  51. 51. Significantly improve many applications on multiple domains “deep learning” trend in the past 10 years image understanding speech recognition natural language processing … Deep Learning autonomy
  52. 52. Autonomous Driving Systems
  53. 53. Real Time, Per Pixel Object Segmentation
  54. 54. Centimeter-accurate positioning
  55. 55. Deploy Everywhere Beyond BlindTool by Joseph Paul Cohen, demo on Nexus 4 Fit the core library with all dependencies into a single C++ source file Easy to compile on … Amalgamation Runs in browser with Javascript The first image for search “dog” at images.google.com Outputs “beagle” with prob = 73% within 1 sec
  56. 56. TX1 on Flying Drone TX1 with customized board Drone Realtime detection and tracking on TX1 ~10 frame/sec with 640x480 resolution
  57. 57. New P2 Instance | Up to 16 GPUs §This new instance type incorporates up to 8 NVIDIA Tesla K80 Accelerators, each running a pair of NVIDIA GK210 GPUs. §Each GPU provides 12 GiB of memory (accessible via 240 GB/second of memory bandwidth), and 2,496 parallel processing cores. §Available in PDX, IAD and DUB RegionsInstance Name GPU Count vCPU Count Memory Parallel Processing Cores GPU Memory Network Performance p2.xlarge 1 4 61 GiB 2,496 12 GiB High p2.8xlarge 8 32 488 GiB 19,968 96 GiB 10 Gigabit p2.16xlarge 16 64 732 GiB 39,936 192 GiB 20 Gigabit
  58. 58. One-Click GPU Deep Learning AWS Deep Learning AMI Up to~40k CUDA cores MXNet TensorFlow Theano Caffe Torch Pre-configured CUDA drivers Anaconda, Python3 + CloudFormation template + Container Image
  59. 59. AWS Marketplace Discover, Procure, Deploy, and Manage Software In the Cloud • 3,800+ software listings • Over 1,200 participating ISVs • Open source and commercial software • Bring-your-own-license • Procure new • Deployed in Most AWS Regions • 135,000+ active customers • Over 370M of deployed EC2 instances per month
  60. 60. MXNet: Scalable Deep Learning Framework
  61. 61. Thank you! Denis V. Batalov, PhD AWS Solutions Architect, EMEA @dbatalov

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