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Building intelligent applications using AI services

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Building intelligent applications using AI services

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Building intelligent applications using AI services

  1. 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Build Intelligent Applications Using AI Services Prakash Palanisamy Solutions Architect Amazon Web Services A I M 0 0 1
  2. 2. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Put machine learning in the hands of every developer Our mission at AWS
  3. 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 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. 4. Some of our machine learning customers…
  5. 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AI Services R E K O G N I T I O N I M A G E P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E XR E K O G N I T I O N V I D E O F O R E C A S TT E X T R A C T P E R S O N A L I Z E VISION SPEECH LANGUAGE CHATBOTS FORECASTING RECOMMENDATIONS ML Services Amazon SageMaker Ground Truth Notebooks Algorithms + Marketplace Reinforcement Learning Training Optimization Deployment Hosting ML Frameworks + Infrastructure EC2 P3 & P3dn EC2 C5 FPGAs Greengrass Elastic inference FRAMEWORKS INTERFACES INFRASTRUCTURE Inferentia EC2 G4
  6. 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 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 A I S E R V I C E S R E O G N I T I O N I M A G E P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D & C O P R E H E N D M E D I C A L L E XR E K O G N I T I O N V I D E O Vision Speech Chatbots F O R E C A S TT E X T R A C T P E R S O N A L I Z E Language Forecasting Recommendations
  7. 7. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Real-time personalization and recommendation service, based on the same technology used at Amazon.com. No ML experience required.
  8. 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Users increasingly expect every interaction to be personalized Activity & Product Recommendation Search Personalization Personalized Notifications Emails
  9. 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Personalization offers material business results Engagement (up to 15% increase) Product Discovery (up to 80% clicks on tail items) Revenue (up to 5% increase) Conversion (up to 30% increase)
  10. 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Effective personalization involves multiple hard problems Popularity Trap Naïve models give recommendations similar to popular items Cold Starts New users should get relevant recommendations, new items should show in recommendations Scale Recommendations should scale across millions of users and items Real-Time Personalization must be responsive to the changing user intent Custom models Personalization models must accurately reflect business context and user behavior
  11. 11. Amazon Personalize: machine learning personalization and recommendations Articles, products, videos, etc. Age, location, etc. Amazon Personalize Customized personalization & recommendation API Views, signups, conversion, etc.
  12. 12. Amazon Personalize: machine learning personalization and recommendations Customized personalization & recommendation API F u l l y m a n a g e d b y A m a z o n P e r s o n a l i z e Amazon Personalize INSPECT DATA IDENTIFY FEATURES SELECT ALGORITHMS SELECT HYPERPARAMETERS TRAIN MODELS OPTIMIZE MODELS HOST MODELS BUILD FEATURE STORE CREATE REAL-TIME CACHES Activity stream from app Inventory Demographics (optional) DeepFM | FFNN | HRNN | Popularity-Count | Personalized Ranking | SIMS
  13. 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Predictor Metrics Metric Name Explanation Example Normalized discounted cumulative gains @ K Considers positional effects by applying inverse logarithmic weights based on the positions of relevant items, normalized by ideal recommendations. ! "#$ !%& ' ! "#$ !%( ' ! "#$ !%) ! "#$ !%! ' ! "#$ !%& ' ! "#$ !%( = 0,71 Precision @ K Total relevant items divided by total recommended items. * + = 0,6 Mean reciprocal ranks @ K Considers positional effects by computing the mean of the inverse positions of all relevant items. ,-./( 1 2 + 1 * + 1 + ) = 0,344 for each of these, higher numbers are better
  14. 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Improve customer experiences with personalization and recommendations Real-time Works with almost any product or content K E Y F E A T U R E S Responsive to changes in intent Automated machine learning Bring existing algorithms from Amazon SageMaker Deliver high quality recommendations Deep learning algorithms Easy to Use
  15. 15. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  16. 16. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Accurate time-series forecasting service, based on the same technology used at Amazon.com. No ML experience required.
  17. 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T The perils of poor predictions in forecasting… Time Forecast Sales Sales Forecasting too low results in opportunity cost, disappointed customers. Forecasting too high results in higher costs for customers and excess inventory.
  18. 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Traditional methods struggle with generating accurate forecasts Can’t handle seasonality Don’t consider related variables such as price, holiday and promotions, that impact forecast accuracy Can’t handle new items, that don’t have historical time-series data
  19. 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Forecast Accurate time series forecasting service, based on the same technology used at Amazon.com. No machine learning experience required. • Draws from 20 years of experience in forecasting at Amazon • Packages a suite of 8 algorithms that includes 5 deep-learning algorithms and 3 statistical methods. The deep-learning algorithms improve accuracy by up to 50%, for datasets with over 1000 time-series. ARIMA | DeepAR+ | ETS | MDN | MQRNN | NPTS | Prophet | SQF
  20. 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Historical data Supply chain, inventory, etc. Customized forecasting API Related “causal” data Weather, special offers, product details Amazon Forecast Amazon Forecast: Machine learning time-series forecasting
  21. 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Historical data Supply chain, inventory, etc. Customized forecasting API Inspect data Identify features Select from 8 algorithms Select hyperparameters Host models Load data Train models Optimize models Related “causal” data Weather, special offers, product details F u l l y m a n a g e d b y A m a z o n F o r e c a s t Amazon Forecast Amazon Forecast: Machine learning time-series forecasting
  22. 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Predictor Metrics Root Mean Square Error (RMSE): • Measures the difference between the values predicted by the model and the actual values in the test dataset. 1 " # $%& ' ()*+", − ./0)1+23 4 Prediction Quantiles: • Quantile loss (QL) calculates how far off the forecast is from actual demand in either direction as a percentage of demand on average in each quantile. 56 7 = 2 . ∑ 7. max ()*+", − ./0)1+23, 0 + 1 − 7 . max ./0)1+23 − ()*+",, 0 ∑ ()*+",
  23. 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Forecast: Machine learning time-series forecasting Any historical time-series Export to CSV to Integrate with SAP and Oracle Supply Chain Custom forecasts with 3 clicks Up to 50% more accurate 1/10th the cost Retail demand Travel demand AWS usage Revenue forecasts Web traffic Advertising demand Generate forecasts for:
  24. 24. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  25. 25. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. OCR++ service to easily extract text and data from virtually any document. No ML experience required.
  26. 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T How documents are processed today Optical Character Recognition (OCR) Manual processing Rules and template-based extraction
  27. 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Textract Features Text extraction Table extraction Form extraction • No code or templates to maintain • Lower document processing costs (only $1.50/1,000 documents)
  28. 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Textract - Text Extraction Blocks: PAGE, PARAGRAPH, LINE, WORD is washed by waves, and cooled
  29. 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Textract - Table Extraction Blocks: PAGE, TABLE, CELL For each ’block’ you get: • Text • Confidence score • Block relationships (e.g. cells within a table)
  30. 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Textract - Form extraction Blocks: PAGE, KEY_VALUE_SET For each ’block’ of your document: • Form field name (key) and field value (value) association • Confidence score • Page number • Block relationships
  31. 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Supports single-page documents such as images (e.g., mobile capture) For multi-page documents, up to 3,000 pages Amazon Textract Sync and Async
  32. 32. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  33. 33. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Easily add intelligent image and video analysis to your applications.
  34. 34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Rekognition: Deep Learning-Based Image and Video Analysis
  35. 35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Rekognition Benefits Low cost Your data is your ownServerless Rapid integration State of the art capabilities Continuous improvement
  36. 36. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Discover insights and relationships in text
  37. 37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Comprehend Di s c o v e r i n s i g h t s a n d r e l a t i o n s h i p s i n t e x t Entities Key Phrases Language Sentiment Syntax Grouping
  38. 38. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Accurately extract health information from patient notes, clinical trial reports, and other electronic health records using Amazon Comprehend
  39. 39. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Comprehend Medical Entities Medication Medical condition Test, treatments, and procedures anatomy Protected Health Information (PHI) Relationship extraction Medication Test, treatments, and procedures Entity traits Negation Diagnosis signs and symptom
  40. 40. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  41. 41. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Turn text into lifelike speech using deep learning
  42. 42. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Polly – Use Cases Contact Centers Special Needs AI Assistant Voiced videos and presentations Language learning Amazon Polly Navigation Podcasting, Voiced blogs and news articles
  43. 43. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Conversational interfaces for your applications powered by the same deep learning technologies as Alexa
  44. 44. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Lex – use cases CONTACT CENTER BOTS Customer service IVR Account inquiries Bill payments Service updates Single Sign On Users / Roles Groups Auditing / Monitoring Risk & Compliancy Insights SECURITY INFORMATIONAL BOTS Answer questions News updates Weather information Game scores APPLICATION BOTS Conversational interfaces Book tickets Order food Manage bank accounts Single Sign On Users / Roles Groups Auditing / Monitoring Risk & Compliancy Insights SECURITY PRODUCTIVITY BOTS Enterprise efficiencies Check sales numbers Inventory status Expense reports IoT BOTS Device interactions Kiosks Appliances Auto A service for building conversational interfaces into your applications using voice and text
  45. 45. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Natural and accurate language translation
  46. 46. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 21 Languages 417 Combinations Key Features Real-time < 500ms / sentence on average < 150ms / conversational / short form Tag Handling XML tags placement maintains styling and formatting through translation < / > Data Security Data ownership Encryption Access Management Ease of Use Simple API calls and partner solutions $15/1M characters Or $0.000075 per word; Pay as you go, 2M characters monthly free tier HIPAA Eligible
  47. 47. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Automatic speech recognition
  48. 48. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Transcribe – Key Features Channel Identification Custom vocabulary Speaker Identification Word-level time stamps Punctuation and capitalization Word-level confidence scores
  49. 49. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  50. 50. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Live Streaming with Automated Multi-Language Subtitling Refer: https://github.com/awslabs/live-streaming-with-automated-multi-language-subtitling
  51. 51. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Document extraction for NLP Quickly turn extracted text/data into actionable insights Input Uploaded document images of medical notes, explanation of benefits, and patient forms Amazon S3 Uploaded documents are stored in S3 Amazon Comprehend Use natural language processing to extract insights from medical documents Amazon Elastisearch Service Easily search through extracted data and text insights Output Discover medical insights to improve patient care Amazon Textract Automatically extract words and lines of text, and tables
  52. 52. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Translator Chatbot Amazon S3 Website AWS Lambda Amazon DynamoDB Amazon Lex Amazon Polly Amazon Translate Amazon Cognito Translation bot Synthesize speech Get Translation Cached Translation Refer: https://aws.amazon.com/blogs/machine-learning/create-a-translator-chatbot-using- amazon-translate-and-amazon-lex/
  53. 53. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  54. 54. Thank you! S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Prakash Palanisamy pprakash
  55. 55. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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