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AWS Summit Singapore 2019 | Building Business Outcomes with Machine Learning on AWS

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Speaker: Barnam Bora, Head of AI/ML, APAC, AWS

Customer Speaker: Guangda Li, Co-founder & CTO, ViSenze
Note: This is part 2 of the deck.

WS offers different paths for building and deploying scalable ML solutions. This session provides an insight to how AWS customers are building intelligent systems powered by AI and ML. Learn how these services, in conjunction with the large number of complementary AWS technologies, provide a great platform for our customers to build their own AI and ML powered solutions and drive business value. Towards the latter part of this session, hear how customers are deploying their ML on AWS and can now leverage Marketplace to monetise their models.

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AWS Summit Singapore 2019 | Building Business Outcomes with Machine Learning on AWS

  1. 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Building Business Outcomes with AI & Machine Learning on AWS Barnam Bora Head of AI & Machine Learning – Asia-Pacific Amazon Web Services Guangda Li Co-founder & CTO ViSenze
  2. 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Customers need a complete end-to-end Machine Learning stack M L F R A M E W O R K S & I N F R A S T R U C T U R E A I S E R V I C E S M L S E R V I C E S A M A Z O N S A G E M A K E R G R O U N D T R U T H A L G O R I T H M S N O T E B O O K S M A R K E T P L A C E U N S U P E R V I S E D L E A R N I N G S U P E R V I S E D L E A R N I N G R E I N F O R C E M E N T L E A R N I N G O P T I M I Z A T I O N ( N E O ) T R A I N I N G H O S T I N G D E P L O Y M E N T Frameworks Interfaces Infrastructure 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 Vision Speech Language Chat-bots Forecasting Recommendations T E X T R A C T F O R E C A S T P E R S O N A L I Z E E C 2 P 3 & P 3 D N E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C I N F E R E N C E I N F E R E N T I A
  3. 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Customers need a complete end-to-end Machine Learning stack M L F R A M E W O R K S & I N F R A S T R U C T U R E A I S E R V I C E S M L S E R V I C E S A M A Z O N S A G E M A K E R G R O U N D T R U T H A L G O R I T H M S N O T E B O O K S M A R K E T P L A C E U N S U P E R V I S E D L E A R N I N G S U P E R V I S E D L E A R N I N G R E I N F O R C E M E N T L E A R N I N G O P T I M I Z A T I O N ( N E O ) T R A I N I N G H O S T I N G D E P L O Y M E N T Frameworks Interfaces Infrastructure 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 Vision Speech Language Chat-bots Forecasting Recommendations T E X T R A C T F O R E C A S T P E R S O N A L I Z E E C 2 P 3 & P 3 D N E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C I N F E R E N C E I N F E R E N T I A
  4. 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon Kinesis AWS Glue Amazon S3 Amazon Glacier Amazon ECS Amazon EC2 Lambda function Amazon DynamoDB Amazon RDS Amazon Redshift Amazon Athena Amazon ES Amazon EMR Amazon QuickSight Amazon Kinesis Analytics Amazon Kinesis Streams Amazon SageMaker AI Services AWS Batch AWS IoT AWS Greengrass Lambda function Ingest Store Compute Database Analyze Intelligence The Edge ML is only a component of a overall complete solution. A W S D E E P L E N S and Many more…
  5. 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Our Customers are actively Building innovative ML based Intelligent systems to empower their businesses, at a global scale through AWS
  6. 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Deployment time down by 90% with Amazon SageMaker < 1 W E E K6 M O N T H S BUILDERS can BUILD Intelligent Systems much faster with AWS….
  7. 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
  8. 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
  9. 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T “소니는 Greengrass를 활용해서 공장 기계에 장착된 센서 Data를 모아 각 장비의 실시간 상태를 파악하고 Machine Learning을 활용하여 Predictive Maintenance에 활용하기 시작했다.” -Ryuji Takehara, Cloud System Architect, Sony • Predictive Maintenance à Bearing 기기노후화로 인한 Performance 저하 파악 • 향후, Factory Solution외 타 IoT 솔루션으로 확장할 계획 • AWS Cloud 환경 내 ML Model Training • Model을 High Frequency 데이터 처리용 보드에 Greengrass로 내려 Inference 수행 • Greengrass를 통해 Model의 지속적인 개선 및 Deploy를 쉽게 할 수 있음 • 공장 내 다양한 제조 장비가 존재하여 기기別 Customize된 ML Model 개발 중à수백 개의 Model 개발/관리에 어려움 • 가속 센서 기반 Anomaly Detection 위해 High Frequency(10KHz↑) 센서데이터 필요 9 Greengrass 기반 Edge Computing à‘Anomaly Detection’ Factory Use Case 구현
  10. 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T 10 “과거의 매뉴얼 Inspection은 30분동안 겨우 50개의 Chip을 검사할 수 있었고, 타 장비와 연계되어 있지 않아 통합적인 분석이 어려웠다" – CTO of Nanotronics Wafer Level Inspection에 ML 적용을 검토해 볼 수 있음 • AI/ML 기반 Optical Inspection 장비로 Defect를 자동으로 발견, 수 분 내에 10만개의 Chip을 검사하여 Time-to-Market을 제고 Optical Inspection 장비 AI/ML 기반 Defect Analyzer • 공정 전반에 배치되어 있는 타 Optical Inspection 장비와 연계하여 통합적인 Defect 분석이 가능함 ü 자체 보유한 ML기반 Display Panel Inspection S/W를 클라우드 환경에서 Training 하는 PoC를 검토 중임
  11. 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Tensorflow AWS Rekognition Amazon Polly Safety Helmet Recognition Facial Recognition Voice Message + “David, Please wear a helmet!” Recognition Alerts Side view Front view
  12. 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T More ML happens on AWS than anywhere else
  13. 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Let’s hear from…
  14. 14. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  15. 15. Visual Search and Recognition at ViSenze Guangda LI CTO and Co-founder www.visenze.com
  16. 16. • Started as Spin off from NExT(NUS-Tsinghua Research centre); Raised 34M USD funding from VCs • Based on computer vision and deep learning research, ViSenze provides visual search and image recognition solution for some of the largest companies in the world • Customers include the top fast fashion retailer, sports brand and mobile phone manufacturer ViSenze has been recognized amongst: • Top 5 deep learning companies - VentureBeat 2017 • Top AI Product AI (Retail) – CogX London 2017 • Top 40 global Breakthrough Brands - InterBrand 2017 About ViSenze Top 25 AI companies in 2018
  17. 17. Under the Hood • Built by Computer Vision scientists and software development experts (40+ R&D in CV, ML, Infra) • 4 patent applications (granted and pending) in various stages • Global scale distributed architecture supporting over 1 Billion • Low latency and high-throughput architect design • In-house distributed GPU training platform and tool development • Independent validations: ImageNet, client evaluations Deep Learning & Computer Vision AI DevOps Platform Continuous Training Domain Models ViSenze singled out in keynote speech by Prof Li Fei Fei of Stanford at CVPR 2017 ViSenze partners Nvidia using latest GPUs for image processing and CNN model training
  18. 18. The Rise of Visual Content on Internet Gen Z prefers to communicate with Images Image Growth - More than 3B photos per day
  19. 19. Major players are already driving this shift of visual search adoption 250M visual searches/month - Pinterest, May 2017 360M visual searches/month - Taobao, July 2017 https://www.forbes.com/sites/kathleenchaykowski/2017/02/08/pinterest-debuts-new-camera-lens-search-tools-to-find-real-world-objects-online/#3223bbc060e1 Amazon Visual Search Pinterest Lens Samsung Bixby Vision Google Lens
  20. 20. H&M, ASOS and UNIQLO Visual Search powered by ViSenze Image Search is now a common feature on leading retailer apps
  21. 21. How It Works : Product recommendation
  22. 22. Improving User Engagement Through Artificial Intelligence A.I. powered visual search and recognition solutions improve engagement and conversions 30% 50% 5x 160% higher conversions on image search over text based search higher CTR of shoppers who click on visually similar products higher conversion rates for shoppers clicking on visually similar products increase in engagement for shoppers who used find similar
  23. 23. Platform Solution: ViSenze Shopping Lens on Smartphones 200M+ Products from over 800+ major global retailer on ViSenze global affiliate network
  24. 24. Query image v[1:4096] Image Database v1 v2 … vN Features (AlexNet) Similarity + kNN (Cosine similarity) Visual Search 1-2-3
  25. 25. Query time Index time Offline training shoe Detection model Search Index Objects Embedding models Extract features Reference images Compression codebook Hash model Extract features NN search and Re- ranking Ranked results Visual Search in ViSenze Deep learning based training
  26. 26. Tagging Solutions and Use Cases Automated Tagging Catalogue Management Tag entire image libraries based on visual attributes for better search results Image Filtering and Moderation Tag entire image libraries based on visual attributes Improve discoverability and conversions Fashion Attributes Style Attributes Image Quality
  27. 27. Tagging Solutions and Use Cases - Example Fashion Attribute Fashion style & Occasion
  28. 28. Fine-grained Fashion Attribute Recognition 25 types of neckline: zip_neck henley off_shoulder scoop crew_neck round_neck v_neck cowl turtleneck ... peter_pan keyhole one_shoulder tie_up_neck other 60 types of product categories: sweater sweatshirt_hoodies blouse shirt t_shirt polo_shirt top camisole tank_top … clutch_purse card_holder pouch wallet others 14 types of product pattern: stripes text animal_print big_graphic ... plaid solid other
  29. 29. Life of A ML pipeline: Continuous Data Cleaning Data Management Challenges in Production Machine Learning, Alkis Polyzotis etc 2017, SIGMOD
  30. 30. The Gap between DL Software and Production DL System TFX: A TensorFlow-Based Production-Scale Machine Learning Platform D. Baylor etc KDD, 2017. Deep learning framework is a small component for production level deep learning development
  31. 31. End-end Deep Learning Platform to Accelerate the Model Development
  32. 32. Hidden Facts for Production Deep learning Development Key winning formula for DL-based applications • Transfer domain Expertise into clear requirement • Large scale high quality training data and data management • Intensive and faster feedback Continuous performance improvement • Hard to result from methodology improvement • Usually results from data driven improvement
  33. 33. DNN Inference Engine on AWS ● Caffe/MXNet/Caffe2 model support ● Batch inference ● TensorRT for Nvidia GPU/OpenVINO for Intel CPU ● Deployment on AWS P2/C series ● scale to 4000+ CPUs, with peak throughput 5M images/hour.Based on the price, choose the most cost-efficient instance type.
  34. 34. Deployment of Models to AWS Marketplace
  35. 35. Visual Search and Recognition at ViSenze Guangda LI guangda@visenze.com www.visenze.com
  36. 36. Thank you! S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Barnam Bora & Guangda Li

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