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Build in 2019 建立分佈式、開放式、數據中心的人工智慧數據驅動平台

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Build in 2019 建立分佈式、開放式、數據中心的人工智慧數據驅動平台

  1. 1. Innovate in 2019 Artificially Intelligent, Constantly Connected and Intuitive at Hyper-Scale Olivier Klein Head of Emerging Technologies Asia-Pacific
  2. 2. Technology is changing the world
  3. 3. ..and the world of technology is changing
  4. 4. INTUITIVE AND HUMAN CENTRIC INTERFACES
  5. 5. ARTIFICIAL INTELLIGENCE AND DATA-DRIVEN DECISIONS U R G E N T X-Ray analysis GAN rendered dental implants Blood sugar measurements through breath MRI scans analysis Deep Learning assisted ultra-sound
  6. 6. ALWAYS CONNECTED AND ACCESSIBLE Intelligent and connected farming Insurance via APIs Medical assistance at your fingers
  7. 7. HUMAN CENTRIC DA TA, C OMPUTE A ND C ONNECTIVITY A T HYPERSCALE A R T IFICIAL I NTELLIGENCE & M A CHINE L EARNING
  8. 8. Alexa, Hello!
  9. 9. Technical & Business Support Marketplace 7 SERVICES Analytics 11 SERVICES IoT 11 SERVICES Machine Learning 20 SERVICES Core Services 5 SERVICES Management Tools 8 SERVICES DevOps 11 SERVICES Blockchain 2 SERVICES Mobile Services 8 SERVICES App Services 5 SERVICES Infrastructure 7 SERVICES Enterprise Apps 9 SERVICES Migration 7 SERVICES Security & Compliance 19 SERVICES 9 SERVICES Lower the cost of experimentation with the broadest and deepest platform for today’s builders
  10. 10. Containers / Serverless Web Standards Frameworks APIs / Microservices CLIENT PORTABILITY BACKEND PORTABILITY INFRA PORTABILITY SDKs Cross-Platform Native Apps
  11. 11. Let’s create a virtual art piece bit.ly/awscube
  12. 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. 如何利用 AWS 導入 DevOps 實戰雙11活動 Andrew Wu Chief Architect 91APP / R&D
  13. 13. 台灣最大&成長最快 品牌新零售解決方案公司 - 2013年成立,前Yahoo!、興奇科技經營團隊創辦 - 總部在台北,馬來西亞/香港分公司 - 員工人數超過350人 - 連續四年榮獲「創新商務獎/最佳商業模式」 - 獲選「勤業眾信亞太區高科技高成長前500強」 (Ranked 152th,Deloitte Technology Fast 500 Asia Pacific) 虛實融合OMO最佳夥伴
  14. 14. 品牌新零售解決方案 提供一站式品牌新零售解決方案,協助零售企業整合技術、運用數據、掌握會員, 建立虛實融合場景,加速實現數位轉型。
  15. 15. 協助品牌打造線上電商與門市OMO循環 提供一致化購物體驗,打通全通路會員經營,有效掌握全景數據,提升OMO營運效能。 官網 APP 門市 客人 線上購物門市取貨 APP推播 線上廣告投放 線上發送門市優惠券 門市購物線上購物 LBS推播 門市發送線上折價券 店員 客人 OMO 循環 店員透過「門市小幫手」 引導客人成為OMO會員 APP就是會員卡
  16. 16. 品牌客戶超過10,000家 獲國內外大型實體零售品牌肯定,91APP協助多家企業成功推動OMO變革轉型。
  17. 17. AGILE + DEVOPS 200 人的研發團隊,如何同時滿足所有 10000+ 客戶的需求?
  18. 18. CI/CD 與 DevOps • 狹義 – CI/CD 持續整合/持續發布 DevOps 是dev (開發) 和ops (作業) 的複合字,這是整合開發和 IT 作業的軟體開發實務。 • 廣義 – Culture 文化 是一種重視「軟體開發人員Dev」和「IT運維技術人員Ops」之間 溝通合作的文化、運動或慣例。
  19. 19. DevOps 三步工作法 Business Customer Value Flow Dev Ops 商業價值 使用價值 Time to market 第一步 由左至右建立一條快速、又穩定的工作流,來交付工作價值 流 Flow 系統思維 Biz
  20. 20. 輸 出 入 看板驅動開發將度量融入流程、指標透明化 Pool Design Verify Refine ment User Story Ready 分析 實作 測試 完成 發佈 佈署 運維 監控 完成 研發的Lead Time 維運的Lead Time需求的Lead Time 用戶故事 持續維運創意 看 板 度 量 團 隊 指 標 平均恢復時間(MTTR) 變更失敗率 交付時間 部署頻率 Mid Stream (分類) (中斷) 功能交付 + + = 真實工時 真實工時 真實工時 Total Lead Time Total working Time 跟蹤從失敗中恢復需要多長時間。= 平均交付的失敗率(MTTF)。= = 成功的交付次數。 盡可能多部署更小的部署,減少部署的規模使測試和發佈變得更加容易。 目標是快速發佈程式碼(利特爾法則)。 輸送量指標, 代表 “敏捷性” 穩定性指標, 代表 “品質”
  21. 21. 扛住雙11流量的四大關鍵 • 善用雲端服務的彈性佈署優勢 • 以用戶體驗為優先,採「閘道管制」策略 • 推行DevOps制度:懂得開發,也要懂得維運防守 • 成立雙11特戰小組
  22. 22. 雙11 事前準備
  23. 23. 用戶體驗優先 設計策略 Cache Layer (AWS)Web Layer Load Balancer (AWS ELB) 型錄 NoSQL (DynamoDB) WebAPI 官網 CDN (CloudFront) MemCache (Redis) Storage Layer Filer (MS DFS) Storage (AWS S3) Enduser (官網) APP iOS / Android Database Layer SQL Server RW SQL ReadOnly 結帳流程 Image 圖片 Reverse Proxy Search Service 型錄頁面 相關服務 結帳流程 相關服務 商品出貨 相關服務
  24. 24. 11/11 流量是平日的七倍 11月份的雲端服務費用,較平時增加 37% 10月份的雲端服務費用,較平時增加 15% 交易量較 2017 年雙十一,成長 80% 尖峰時間同時交易人潮超過數萬人
  25. 25. • DEVOPS = 人員 (可信賴的 91APP 研發團隊) + 流程 (落實敏捷的團隊協作) + 技術 (可信賴的 AWS 雲端服務與基礎建設) • Ruddy 老師: 專案執行之初,首重看見全貌。
  26. 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  27. 27. • Map villages in Africa to deliver right amount of vaccines • Provide first responders with information during national disasters • PSMA Australia - 200TB imagery - 7.6 million km2 - 20 million buildings Cloud-based platform to derive insights and convert unstructured satellite imagery into meaningful insights using ML Generates 80TB/day of imagery data
  28. 28. PHOTOGRAMMETRY
  29. 29. DEMO Amazon Sumerian Pictures
  30. 30. EA SI L Y A DD I NTEL L I GENCE TO A P P L I CA TI ONS NO MA CHI NE L EA RNI NG SKI L L S REQUI RED MACH INE L EARN ING F O R EV ERY DEVEL OP ER AN D DAT A SCIEN TIST BU IL D, T RAIN AN D DEPL OY ML F AST CHOI CE A ND FL EXI BI L I TY WI TH BROA DEST FRA MEWORK SUP P ORT FA STEST A ND L OWEST - COST COMP UTE OP TI ONS AI Services ML Services ML Frameworks + Infrastructure
  31. 31. AI Services ML Services ML Frameworks + Infrastructure EC2 P3 & P3dn EC2 C5 FPGAs Greengrass Elastic inference FR A ME WO R KS I NT E R FA CES I N F R A S T R U C T U R E Inferentia EC2 G4
  32. 32. AWS is framework agnostic Run them fully managed Or run them yourself
  33. 33. Running inference in production Inference Training Infrastructure costsDRIVES THE MAJORITY OF COST FOR MACHINE LEARNING
  34. 34. Provision just the amount of GPU you need for faster inference Amazon Elastic Inference Add GPU acceleration to any Amazon EC2 instance Reduce the cost of running deep learning inference by up to 75% Capacity ranging from 1 TFLOPS up to 32 TFLOPS Supports TensorFlow, Apache MXNet, and ONNX models
  35. 35. 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 FR A ME WO R KS I NT E R FA CES I N F R A S T R U C T U R E Inferentia EC2 G4 AI Services
  36. 36. Collect and prepare training data Choose and optimize your ML algorithm Set up and manage environments for training Train and tune model (trial and error) Deploy model in production Scale and manage the production environment Amazon SageMaker BRINGING MACHINE LEARNING TO ALL DEVELOPERS
  37. 37. Choose and optimize your ML algorithm Set up and manage environments for training Train and tune model (trial and error) Deploy model in production Scale and manage the production environment Pre-built notebooks for common problems Collect and p rep are training d ata Amazon SageMaker BRINGING MACHINE LEARNING TO ALL DEVELOPERS Explore data close to where it’s stored Reuse knowledge for common problems
  38. 38. Train and tune model (trial and error) Deploy model in production Scale and manage the production environment Built-in, high performance algorithms Choos e and op timize y ou r ML algorithm K-Means Clustering Principal Component Analysis Neural Topic Modelling Factorization Machines Linear Learner (Regression) BlazingText Reinforcement learning XGBoost Topic Modeling (LDA) Image Classification Seq2Seq Linear Learner (Classification) DeepAR Forecasting Pre-built notebooks for common problems Collect and p rep are training d ata Amazon SageMaker BRINGING MACHINE LEARNING TO ALL DEVELOPERS Train models on your data without writing algorithms Explore or contribute to the ML Marketplace
  39. 39. Train and tune model (trial and error) Deploy model in production Scale and manage the production environment Pre-built notebooks for common problems Collect and p rep are training d ata Built-in, high performance algorithms Choos e and op timize y ou r ML algorithm One-click training Set up and manage environments for training Amazon SageMaker BRINGING MACHINE LEARNING TO ALL DEVELOPERS No more idle resources, pay only per seconds trained
  40. 40. Deploy model in production Scale and manage the production environment Pre-built notebooks for common problems Collect and p rep are training d ata Built-in, high performance algorithms Choos e and op timize y ou r ML algorithm One-click training Set up and manage environments for training Optimization Train and tune model (trial and error) Amazon SageMaker BRINGING MACHINE LEARNING TO ALL DEVELOPERS Perform hyper parameter optimization automatically, effectively allowing more experimentation for better results
  41. 41. Scale and manage the production environment Pre-built notebooks for common problems Collect and p rep are training d ata Built-in, high performance algorithms Choos e and op timize y ou r ML algorithm One-click training Set up and manage environments for training Optimization Train and tune model (trial and error) One-click deployment Deploy model in production Amazon SageMaker BRINGING MACHINE LEARNING TO ALL DEVELOPERS Your trained model becomes another API endpoint Integrate it within your existing apps easily
  42. 42. Pre-built notebooks for common problems Collect and p rep are training d ata Built-in, high performance algorithms Choos e and op timize y ou r ML algorithm One-click training Set up and manage environments for training Optimization Fully managed with auto - scaling, health checks, automatic handling of node failures, and security checks S cale and manag e the prod u ction env ironment Train and tune model (trial and error) One-click deployment Deploy model in production Amazon SageMaker BRINGING MACHINE LEARNING TO ALL DEVELOPERS Automatic monitoring and scaling to what you need
  43. 43. R E K O G N I T I O N I M A G E R E K O G N I T I O N V I D E O T E X T R A C T VISION P O L L Y T R A N S C R I B E SPEECH T R A N S L A T E C O M P R E H E N D LANGUAGE L E X CHATBOTS F O R E C A S T FORECASTING P E R S O N A L I Z E RECOMMENDATIONS AI Services 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 FR A ME WO R KS I NT E R FA CES I N F R A S T R U C T U R E Inferentia EC2 G4
  44. 44. How do you teach machine learning models to make decisions when there is no training data?
  45. 45. Supervised learning Unsupervised learning Reinforcement Learning
  46. 46. Learn by interacting with the real world Model problem as a simulation environment Trial and error Observe results Optimise learning strategy to maximise long-term reward
  47. 47. Vehicle routing Objective Fulfill customer orders STATE Current location, distance from homes … ACTION Accept, pick up, and deliver order REWARD Positive when we deliver on time Negative when we fail to deliver on time Applicable in many domains and industries
  48. 48. Amazon SageMaker RL New machine learning capabilities in Amazon SageMaker to build, train and deploy with reinforcement learning Fully managed RL algorithms TensorFlow, MXNet, Intel Coach, and Ray RL 2D and 3D simulation environments via OpenGym Simulate with Sumerian and AWS RoboMaker Example notebooks and tutorials
  49. 49. DEMO Amazon Sumerian Amazon Sagemaker RL
  50. 50. Build machine learning models in Amazon SageMaker Train, test, and iterate on the track using the AWS DeepRacer 3D racing simulator OpenVino to accelerate Deep Learning models for the underlying hardware AWS DeepRacer A fully autonomous 1/18th-scale race car designed to help you learn about reinforcement learning through autonomous driving
  51. 51. Build reinforcement learning model DeepRacer League Races at AWS Summits Winners of each DRL Race and top scorers compete in Championship Cup at re:Invent 2019 Virtual tournaments through the year AWS DeepRacer League World’s first global autonomous racing league
  52. 52. and technology is changing the world The world of technology is changing
  53. 53. GO BUILD!

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