AWS_HK_StartupDay_Building Interactive websites while automating for efficiency with Amazon AI Services

1. © 2020, Amazon Web Services, Inc. or its affiliates. All rights reserved. Building interactive websites while automating for efficiency with Amazon AI services Clifford Duke Solutions Architect, AWS

3. Our approach for machine learning Customer-focused 90%+ of our ML roadmap is defined by customers Multi-framework Support for the most popular frameworks Pace of innovation 200+ new ML launches and major feature updates in the last year Breadth and depth A wide range of AI and ML services in- production Security and analytics Deep set of security and encryption features, with robust analytics capabilities Embedded R&D Customer-centric approach to advancing the state of the art

4. The AWS ML Stack Broadest and most complete set of Machine Learning capabilities VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS Ground Truth AWS Marketplace for ML Neo Augmented AIBuilt-in algorithms Notebooks Experiments Processing Model training & tuning Debugger Autopilot Model hosting Model Monitor Deep Learning AMIs & Containers GPUs & CPUs Elastic Inference Inferentia FPGA Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Comprehend +Medical Amazon Translate Amazon Lex Amazon Personalize Amazon Forecast Amazon Fraud Detector Amazon CodeGuru AI SERVICES ML SERVICES ML FRAMEWORKS & INFRASTRUCTURE Amazon Textract Amazon Kendra Contact Lens For Amazon Connect SageMaker Studio IDE Amazon SageMaker DeepGraphLibrary RL Coach

5. Fully managed data processing jobs and data labeling workflows One-click collaborative notebooks and built-in, high performance algorithms and models One-click training Debugging and optimization One-click deployment and autoscaling Amazon SageMaker helps you build, train, and deploy models Visually track and compare experiments Automatically spot concept drift Fully managed with auto-scaling for 75% less Prepare Build Train & Tune Deploy & Manage 101011010 010101010 000011110 Collect and prepare training data Choose or bring your own ML algorithm Set up and manage environments for training Train, debug, and tune models Deploy model in production Manage training runs Monitor models Validate predictions Scale and manage the production environment Add human review of predictions Web-based IDE for machine learning Automatically build and train models

7. AI Services Pre-trained AI services that require no ML skills or training Easily add intelligence to your existing apps and workflows Quality and accuracy from continuously-learning APIs VISION SPEECH TEXT SEARCH CHATBOTS PERSONALIZATION FORECASTING FRAUD DEVELOPMENT CONTACT CENTERS Amazon Rekognition Amazon Polly Amazon Transcribe +Medical Amazon Comprehend +Medical Amazon Translate Amazon Lex Amazon Personalize Amazon Forecast Amazon Fraud Detector Amazon CodeGuru Amazon Textract Amazon Kendra Contact Lens For Amazon Connect

9. Amazon Rekognition – Image and Video Analysis

10. Object & scene detection

11. Facial analysis

12. Face search/comparison

13. Use case? Moderating user-generated content

14. Policing user-generated content Age range – 26–43 years Wearing glasses – 99.9% Eyes closed – 94% Mouth open – 96% Eyes closed – 94% Barrack Obama – 100% Not smiling – 60.3% Female – 100%

15. Challenges of non-AI approach • Manual process for checking images – Labor intensive • Non-uniformity – Results vary from resource to resource • Scalability – Difficult to keep up with the rate of image generation

16. Example: user-generated content moderation 2. Submit picture 4. DetectFaces 8. SearchFaces - Blacklist - Whitelist - Duplicate check - Persons of interest 1. Live pic 3. Store live pic Amazon Rekognition Lambda Step functions 5. Recognize Celebrities Amazon Rekognition 7. Detect Moderation Labels 9. Store metadata and analysis Amazon DynamoDB Elasticsearch Blacklist images Amazon Rekognition Amazon Rekognition

17. Amazon Textract – OCR++

18. Amazon Textract – How it works

19. Use case? Automate traditional document processing

20. Example: automated document processing 2. Extract form data 1. Capture document image Amazon Textract Application Backend 3. Send data to backend 4. User submitted data loaded into database Amazon DynamoDB

23. Amazon Lex – Features Text and speech language understanding: powered by the same technology as Amazon Alexa Deployment to chat services (Web/Mobile Apps, Facebook, Kik, Slack, Twilio SMS) Designed for builders: efficient and intuitive tools to build conversations; scales automatically Versioning and alias support@

24. Amazon Lex Bots – key concepts Utterances Spoken or typed phrases that invoke your intent BookHotel Intents An intent performs an action in response to natural language user input Slots Slots are input data required to fulfill the intent Fulfillment Fulfillment mechanism for your intent

25. “Book a hotel” Book hotel NYC “Book a hotel in NYC” Automatic speech recognition Hotel booking New York City Natural language understanding Intent/slot Model UtterancesHotel Booking City New York City Check in Nov 30th Check out Dec 2nd “Your hotel is booked for Nov 30th” Amazon Polly Confirmation: “Your hotel is booked for Nov 30th” “Can I go ahead with the booking? a in

26. Utterances I’d like to book a hotel Can you help me book my hotel? I want to book a hotel in New York City I want to make my hotel reservations

27. Slots Destination City New York City, Seattle, London … Slot Type Values Check in Date Valid dates Check out Date Valid dates

28. Slot elicitation I’d like to book a hotel What date do you check in? New York City Sure, what city do you want to book? Nov 30th Check in 11/30/2017 City New York City

29. Amazon Connect Self-service, cloud-based contact center service Real time and historical analytics High-quality voice capability Call recording Skills-based routing [Automatic Call Distribution (ACD)]

30. Intelligent call center chatbot Amazon Connect Customer Amazon Lex Lambda: Fulfillment DynamoDB: Customer Data SNS: SMS Messaging Customer calls Connect to reschedule an appointment Connect calls Lex chatbot Lex chatbot calls Lambda function to get customer preferences and fulfil Intents Lambda function sends text message confirmation via SNS Customer receives appointment confirmation text message Lambda function writes updates to DynamoDB

34. Amazon Comprehend Sentiment Entities LanguagesKeyphrases Topic modeling Syntax

35. Amazon Comprehend – Natural Language Processing Amazon.com, Inc. is located in Seattle, WA and was founded July 5, 1994 by Jeff Bezos. Our customers love buying everything from books to blenders at great prices Named Entities • Amazon.com: Organization • Seattle, WA : Location • July 5th,1994: Date • Jeff Bezos : Person Keyphrases • Our customers • books • blenders • great prices Sentiment • Positive Language • English

36. Amazon Comprehend – Syntax API Our customers love buying everything from books to blenders at great prices Token (word) Part of Speech customers Noun love Verb books Noun great Adjective prices Noun

37. Supported parts of speech ADJ – Adjective ADP – Adposition ADV – Adverb AUX – Auxiliary CCONJ – Coordinating Conjunction DET – Determiner INTJ - Interjection NOUN - Noun NUM – Numeral O – Other PART – Particle PRON – Pronoun PROPN – Proper Noun PUNCT – Punctuation SCONJ – Subordinating Conjunction SYM – Symbol VERB – Verb

38. Syntax detection $ aws comprehend detect-syntax --language-code 'en' --text 'I love cloud!’ { "SyntaxTokens": [ { "TokenId": 1, "Text": "I", "BeginOffset": 0, "EndOffset": 1, "PartOfSpeech": { "Tag": "PRON", "Score": 0.9999802112579346 } }, ...

39. Sentiment analysis $ aws comprehend detect-sentiment --language-code 'en' --text 'I love cloud!’ { "Sentiment": "POSITIVE”, "SentimentScore": { "Mixed": 0.012617903761565685, "Positive": 0.9599817991256714, "Neutral": 0.021758323535323143, "Negative": 0.005641999188810587 } }

40. Popular text analytics use cases Content Personalization • Understand related documents based on entities, phrases or even topic similarities for trends analysis, to drive content personalization and recommendations Semantic Search • Index entities for boosting and ranking search results Intelligent data warehouse • Query unstructured data in relational databases, processing data within the data lake (Amazon S3) and then inserting it back into the data warehouse Social Analytics • Ingest, process and analyze trends from entities and sentiment from social media posts across Twitter and Facebook

41. Support for large data sets and topic modeling STORM WORLD SERIES STOCK MARKET WASHINGTON LIBRARY OF NEWS ARTICLES * Amazon Comprehend

42. Audio Input Example: End-to-end audio analysis store Amazon S3 trigger AWS Lambda call Amazon Transcribe in cascade Amazon Comprehend aggregate Amazon Athena analyze Amazon QuickSight

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