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Machine Learning at the IoT Edge (IOT214) - AWS re:Invent 2018

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Whether it's connected cars, smart home devices, or industrial applications, IoT applications are rapidly becoming more intelligent. Edge computing is helping lead this transformation as IoT devices not only collect and transmit data, but also perform predictive analytics and respond to local events, even without cloud connectivity. In this session, learn about ML inference at the edge, why it matters, and how to use it to build intelligent IoT applications. Through customer use cases, we demonstrate how to use AWS Greengrass to locate cloud-trained ML models, deploy them to your AWS Greengrass devices, enable access to on-device computing power, and apply the models to locally generated data without connection to the cloud.

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Machine Learning at the IoT Edge (IOT214) - AWS re:Invent 2018

  1. 1. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine Learning at the IoT Edge David Nunnerley AWS Senior Manager AWS IoT Greengrass I O T 2 1 4 Nobutaka Nakazawa CTO Brains Technology, Inc. Masanori Sato Group Manager Aisin AW LTD
  2. 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda Why Machine Learning (ML) at the Edge? AWS IoT Greengrass overview ML Inference at the Edge with AWS IoT Greengrass New AWS IoT Greengrass ML capabilities Customer use case: Aisin AW (Masanori Sato) Brains Technology (Nobutaka Nakazawa)
  3. 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  4. 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Medical equipment Industrial machinery Extreme environments Most machine data never reaches the cloud
  5. 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Why this problem isn’t going away Law of physics Law of economics Law of the land
  6. 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  7. 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS IoT Greengrass
  8. 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS IoT Greengrass All AWS Cloud services e.g., Amazon S3, Amazon Kinesis, Amazon Redshift… AWS IoT services e.g., AWS IoT Core, AWS IoT Analytics, AWS IoT Device Defender…
  9. 9. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data and State Sync Security Over the Air UpdatesConnectors Local Device Shadows Code Deployment Lambda Functions AWS-grade security Easily Update Greengrass Core Machine Learning Inference Local Execution of ML Models Local Resource Access Lambdas Interact With Peripherals Easy integrations with AWS services, protocol adaptors and other SaaS providers Local Messages and Triggers Local Message Broker Manage Secrets at the edge AWS Secrets Manager functionality at edge AWS Greengrass Extend AWS IoT to the Edge
  10. 10. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data and State Sync Security Over the Air UpdatesConnectors Local Device Shadows Code Deployment Lambda Functions AWS-grade security Easily Update Greengrass Core Machine Learning Inference Local Execution of ML Models Local Resource Access Lambdas Interact With Peripherals Easy integrations with AWS services, protocol adaptors and other SaaS providers Local Messages and Triggers Local Message Broker Manage Secrets at the edge AWS Secrets Manager functionality at edge AWS Greengrass Extend AWS IoT to the Edge
  11. 11. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data and State Sync Security Over the Air UpdatesConnectors Local Device Shadows Code Deployment Lambda Functions AWS-grade security Easily Update Greengrass Core Machine Learning Inference Local Execution of ML Models Local Resource Access Lambdas Interact With Peripherals Easy integrations with AWS services, protocol adaptors and other SaaS providers Local Messages and Triggers Local Message Broker Manage Secrets at the edge AWS Secrets Manager functionality at edge AWS Greengrass Extend AWS IoT to the Edge
  12. 12. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data and State Sync Security Over the Air UpdatesConnectors Local Device Shadows Code Deployment Lambda Functions AWS-grade security Easily Update Greengrass Core Machine Learning Inference Local Execution of ML Models Local Resource Access Lambdas Interact With Peripherals Easy integrations with AWS services, protocol adaptors and other SaaS providers Local Messages and Triggers Local Message Broker Manage Secrets at the edge AWS Secrets Manager functionality at edge AWS Greengrass Extend AWS IoT to the Edge
  13. 13. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data and State Sync Security Over the Air UpdatesConnectors Local Device Shadows Code Deployment Lambda Functions AWS-grade security Easily Update Greengrass Core Machine Learning Inference Local Execution of ML Models Local Resource Access Lambdas Interact With Peripherals Easy integrations with AWS services, protocol adaptors and other SaaS providers Local Messages and Triggers Local Message Broker Manage Secrets at the edge AWS Secrets Manager functionality at edge AWS Greengrass Extend AWS IoT to the Edge
  14. 14. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data and State Sync Security Over the Air UpdatesConnectors Local Device Shadows Code Deployment Lambda Functions AWS-grade security Easily Update Greengrass Core Machine Learning Inference Local Execution of ML Models Local Resource Access Lambdas Interact With Peripherals Easy integrations with AWS services, protocol adaptors and other SaaS providers Local Messages and Triggers Local Message Broker Manage Secrets at the edge AWS Secrets Manager functionality at edge AWS Greengrass Extend AWS IoT to the Edge
  15. 15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data and State Sync Security Over the Air UpdatesConnectors Local Device Shadows Code Deployment Lambda Functions AWS-grade security Easily Update Greengrass Core Machine Learning Inference Local Execution of ML Models Local Resource Access Lambdas Interact With Peripherals Easy integrations with AWS services, protocol adaptors and other SaaS providers Local Messages and Triggers Local Message Broker Manage Secrets at the edge AWS Secrets Manager functionality at edge AWS Greengrass Extend AWS IoT to the Edge
  16. 16. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data and State Sync Security Over the Air UpdatesConnectors Local Device Shadows Code Deployment Lambda Functions AWS-grade security Easily Update Greengrass Core Machine Learning Inference Local Execution of ML Models Local Resource Access Lambdas Interact With Peripherals Easy integrations with AWS services, protocol adaptors and other SaaS providers Local Messages and Triggers Local Message Broker Manage Secrets at the edge AWS Secrets Manager functionality at edge AWS Greengrass Extend AWS IoT to the Edge
  17. 17. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data and State Sync Security Over the Air UpdatesConnectors Local Device Shadows Code Deployment Lambda Functions AWS-grade security Easily Update Greengrass Core Machine Learning Inference Local Execution of ML Models Local Resource Access Lambdas Interact With Peripherals Easy integrations with AWS services, protocol adaptors and other SaaS providers Local Messages and Triggers Local Message Broker Manage Secrets at the edge AWS Secrets Manager functionality at edge AWS Greengrass Extend AWS IoT to the Edge
  18. 18. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Data and State Sync Security Over the Air UpdatesConnectors Local Device Shadows Code Deployment Lambda Functions AWS-grade security Easily Update Greengrass Core Machine Learning Inference Local Execution of ML Models Local Resource Access Lambdas Interact With Peripherals Easy integrations with AWS services, protocol adaptors and other SaaS providers Local Messages and Triggers Local Message Broker Manage Secrets at the edge AWS Secrets Manager functionality at edge AWS Greengrass Extend AWS IoT to the Edge
  19. 19. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  20. 20. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Inference Training Machine Learning at the Edge Local actions Edge Cloud
  21. 21. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Inference Training Machine Learning at the Edge Local actions Edge Cloud
  22. 22. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Inference Training Machine Learning at the Edge Local actions Edge Cloud
  23. 23. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Inference Training Machine Learning at the Edge Local actions Edge Cloud
  24. 24. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Inference Training Machine Learning at the Edge Local actions Edge Cloud
  25. 25. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Inference Training Machine Learning at the Edge Local actions Edge Cloud
  26. 26. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS IoT Greengrass Core Machine Learning Run ML Inference on Greengrass Deploy an ML model from Amazon SageMaker in the cloud to a target AWS Greengrass core device using the Greengrass console or Command Line Interface (AWS CLI) Install the necessary run-time for the model e.g., (TensorFlow, Apache MXNet, Chainer…) on the AWS Greengrass core Available for multiple hardware architectures: e.g., Intel x86-64, ARM v7 and Nvidia Jetson TX2 Code your Lambda to read from attached device/sensor (optionally from MQTT topic) and pass to the Lambda running the ML model. Take action based upon the inference.
  27. 27. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS IoT Greengrass Core 1.7 Machine Learning New Machine Learning capabilities Image Classification Connector (available for download from console) Pre-built Lambda to run the Image classification ML model Easy coding to bridge from input device to the supplied Lambda running the inference Image Classification Model can be trained to learn new image classifications in the cloud with Amazon SageMaker
  28. 28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Code to use the Image Classification Connector import greengrass_machine_learning_sdk as ml with open('/test_img/test.jpg', 'rb') as f: content = f.read() def infer(): logging.info('invoking Greengrass ML Inference service') try: resp = client.invoke_inference_service( AlgoType='image-classification', ServiceName='imageClassification', ContentType='image/jpeg', Body=content ) except ml.GreengrassInferenceException as e: logging.info('inference exception {}("{}")'.format(e.__class__.__name__, e)) return except ml.GreengrassDependencyException as e: logging.info('dependency exception {}("{}")'.format(e.__class__.__name__, e)) return logging.info('resp: {}'.format(resp)) predictions = resp['Body'].read() logging.info('predictions: {}'.format(predictions))
  29. 29. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Code to use the Image Classification Connector import greengrass_machine_learning_sdk as ml with open('/test_img/test.jpg', 'rb') as f: content = f.read() def infer(): logging.info('invoking Greengrass ML Inference service') try: resp = client.invoke_inference_service( AlgoType='image-classification', ServiceName='imageClassification', ContentType='image/jpeg', Body=content ) except ml.GreengrassInferenceException as e: logging.info('inference exception {}("{}")'.format(e.__class__.__name__, e)) return except ml.GreengrassDependencyException as e: logging.info('dependency exception {}("{}")'.format(e.__class__.__name__, e)) return logging.info('resp: {}'.format(resp)) predictions = resp['Body'].read() logging.info('predictions: {}'.format(predictions))
  30. 30. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Code to use the Image Classification Connector import greengrass_machine_learning_sdk as ml with open('/test_img/test.jpg', 'rb') as f: content = f.read() def infer(): logging.info('invoking Greengrass ML Inference service') try: resp = client.invoke_inference_service( AlgoType='image-classification', ServiceName='imageClassification', ContentType='image/jpeg', Body=content ) except ml.GreengrassInferenceException as e: logging.info('inference exception {}("{}")'.format(e.__class__.__name__, e)) return except ml.GreengrassDependencyException as e: logging.info('dependency exception {}("{}")'.format(e.__class__.__name__, e)) return logging.info('resp: {}'.format(resp)) predictions = resp['Body'].read() logging.info('predictions: {}'.format(predictions))
  31. 31. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS IoT Greengrass Core 1.7 Machine Learning Other New Machine Learning capabilities Greengrass support for the new Amazon SageMaker Neo (Deep Learning Runtime) Optimize the model using Neo compiler in the cloud More performant without loss of accuracy Smaller memory footprint Deploy optimized Neo model to the Greengrass core device Install Neo run-time to the device Write a Lambda to run the Neo optimized ML model
  32. 32. Smart Vending Machine -- R&D Innovation Team, from the SA Americas organization
  33. 33. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Camera Smart vending machine Object Detection and Image Classification models Load Sensors readings in local time series database Sensor fusion functions to detect removed items and strange objects Thanks for shopping! 3x Water Bottles USD 1.50 each Your total is $4.50
  34. 34. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Our Launch Partners “The addition of AWS IoT Greengrass with its latest ML Inference update running on ADLINK’s industrial vision systems makes for truly plug- and-play IoT. Now when we power-on an off-the-shelf ADLINK NEON smart camera running AWS IoT Greengrass with its latest ML Inference update, we can get to high-quality outcomes much, much faster. This allows us to further speed development of our IoT digital experiments for our logistics, quality inspection, industrial robotics, and other manufacturing customers.” - Elizabeth Campbell, General Manager, The Americas, ADLINK Technology “The potential of computer vision use cases enabled by IoT and AI is vast for businesses to exponentially improve productivity and efficiency. In this time of intelligent transformation, our premium industrial Think IoT cameras powered by AWS IoT Greengrass with the latest machine learning upgrades are engineered to make a notable difference to enterprise customers.” - Jon Pershke, Vice President of Strategy and Emerging Business, Intelligent Devices “The pervasiveness of artificial intelligence and the pace of digital transformation continues to grow at an astonishing rate. Innovations like the newest improvements to AWS IoT Greengrass Machine Learning that markedly decrease latency without decreasing the accuracy of ML inference accelerate new solutions to emerging industrial automation use cases for object identification and classification. AWS’ new machine learning solution integrated with Leopard Imaging’s AICam powered by NVIDIA® GPU will be a cornerstone in any edge to cloud Industrial and Smart City solution.” -Bill Pu, President and Co-Founder, Leopard Imaging “Vieureka of Panasonic is very pleased to utilize the application evolving functions of AWS’s machine learning as enabled by AWS IoT Greengrass. In order to offer Vieureka-Cameras and service management functions to all the partners of the AWS community, I would like to develop a Greengrass compatible version as soon as possible. We will create the environment for developers in the spring of 2019, with commercial versions available in autumn of the same year.” - Miyazaki, CEO of Vieureka Service, Panasonic
  35. 35. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Nobutaka Nakazawa Brains Technology, Inc. CTO Masanori Sato Aisin AW LTD Group Manager
  36. 36. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine state monitoring by cloud & edge computing AISIN AW CO.,LTD. Manufacturing Engineering Development Production System Innovation Group Masanori Sato
  37. 37. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda ・ Company profile ・ About our production engineering ・ Action background ・ Action summary
  38. 38. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. 9,830,000 NAVI Securing a Top Share of the Global Market with Our Innovative Manufacturing World No.1 World No.2 AT ■ Business summary of 2017(in March, 2018) Unit sales AT: 9,830,000units NAVI: 1,810,000units Sales amount A connection: 1,621,200 million yen AISIN AW CO.,LTD Company Profile
  39. 39. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Mission of manufacturing engineering Workplace skills that bringing value: Production engineering Production ◆ Finish of the SE ※ / drawing ※ Simultaneous Engineering ◆ Design of the product line / setup ◆ Design of facilities / production ◆ Development of the new production engineering ◆ Plan, design of the factory Ordering, suggestion Three-Pillar Manufacturing Suggestion Suggestion Suggestion Production engineering Product Design Trading company Equipment manufacturer Delivery of goods, suggestion Cooperate as a partner Vender
  40. 40. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Background of the action Way of thinking of Industrial IoT in AISIN AW Man Machine Material Method Human factor from who involved ・ Assembling ・ Machine setting ・ ・・etc. Factor hardware such as machines ・ Blade tool ・ Metal mold ・ ・・ etc. Factor from Materials (property value) ・ Ductility, toughness ・ Hardness ・ ・・etc. Factor from production method ・ Processing method ・ Processing order ・ ・・etc. Building a base of high level condition monitoring and control by using information technology The production revolution by IT is proposed in the world → Need to develop the Industrial IoT production system for AISIN AW
  41. 41. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Background of the action What we need for I-IoT We have started to develop I-IoT system that utilizes "cloud & edge" that can satisfy these requirements • Small start • Real-time detection • Scalability -Connectable with more than 20,000 machines -Easy deployment to each factory • Successful partner -Quick and challenge
  42. 42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Cloud & edge computing analysis base ◯◯◯ Factory ◯◯◯ Factory Analysis monitor Edge device AWS Services - Storage, Managed Services, etc Cloud Edge device • Dashboard • Simulation model making • Algorithm development • Edge device management Factory ANotice monitor Real-time monitoring & ML detection Factory B Analysis monitor Notice monitor Machines Machines
  43. 43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Cause inquire-able detection ML algorithm Develop machine learning algorithm that person can understand a results and can improve immediately If not If cause is clear Data A Machine learning Not good Data A Machine learning It’s not red enough, and It is not ripe What is ? What part ? I see! I can take action immediately Not good [No reason] [Reason]
  44. 44. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Cause inquire-able detection ML algorithm Good point ・ Got good result in a month after using →Because satisfaction of detection result, it‘s actively used → Leads to expansion ・The model can be constructed with high precision at an early stage →Model making took three months ⇒ one week Difficult point ・Since it’s new initiative, it will not be adopted unless it is indicated by the result →Need one year temporary use for the use at the mass production line
  45. 45. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Outcomes of cloud & edge system in AISIN AW Development of the I-IoT future ・ Further high precision monitoring by algorithm development ・ Expanding to other factories and processes and supervising management ・ Training of workers to increase IoT talent Time[msec] Time[msec] value value unusual point The system detected “anomaly” state and Suppress the cost of long line stop
  46. 46. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine Learning at the IoT Edge Nobutaka Nakazawa CTO Brains Technology, Inc. I O T 2 1 4
  47. 47. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda Company profile Overview of impulse Algorithm Summary
  48. 48. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  49. 49. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Company profile Company name Brains Technology, Inc. Founded August 8, 2008 CEO Sawako Hamanaka Capital 110 million yen (Including capital reserve) Address Shinagawa Center Building 3-23-17, Takanawa, Minato-Ku, TOKYO, Japan URL https://www.brains-tech.co.jp Provide innovative service and bring about technological innovations with open technology Providing innovative service for business enterprises, improve the productivity of corporate activities dramatically Our Mission
  50. 50. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  51. 51. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Overview of Impulse 20 41 84 0 20 40 60 80 100 FY2015 (〜2016/7) FY2016 (〜2017/7) FY2017 (〜2018/7) 145+ Predictive Maintenance Quality Management - Plant equipment(power, chemical, bio) - Co-generation system - Industrial machinery (robots) - Construction machines (crane, elevator) - Electrical equipment (air conditioners, water heaters) - Auto parts (transmissions, gears, drive shafts, bumpers) - Electrical equipment (LED) - Chemical products - Casting Impulse is the IoT ML edge platform for the manufacturing industry for any kind of time series data built on top of AWS. https://impulse-cloud.com
  52. 52. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Architecture File Monitor Output result Raw data Anomaly Detection Post Process Factory AWS IoT Amazon S3 AWS Lambda Amazon DynamoDB AWS Batch UI Dashboard Simulation Line A Output result Line B Raw data Edge PC Greengrass Core Thing Thing Amazon Athena Amazon S3 Raw data Model Amazon SageMaker
  53. 53. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Deploy Models using ML Inference Three modules to deploy ML Library (MXNet, Tensorflow, scikit-learn) AWS Greengrass ML Inference or Lambda Your Code Lambda Model AWS Greengrass ML Inference / S3 Steps to deploy • Create AWS Lambda functions for ML inference. • Create Models by Amazon SageMaker or AWS Batch and upload the models to Amazon S3. • AWS IoT fully manages the whole deployment process. Upload model files to S3 Setting up local resource in AWS IoT
  54. 54. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  55. 55. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Automated algorithm and parameter optimization Analyzing the characteristics of the data and auto-selecting the optimum algorithms and parameters Anomaly detection models Input gaussian periodic correlation independent Mahalanobis S-H-ESD One Class SVM Sparse Coding Recommended parameters Breakout LOF Gaussian Process Data Characteristics Algorithm Parameter Output Recommended parameters Recommended parameters Recommended parameters Recommended parameters Recommended parameters Recommended parameters
  56. 56. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Sparse coding • Sparse coding is a class of unsupervised methods for learning sets of over-complete bases to represent data efficiently • Finds a sparse representation of data against a fixed, precomputed dictionary • Works well for high-speed time- series signals with periodic pattern Dictionary Leaning Decoding from dictionary
  57. 57. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. LOF (Local Outlier Factor) • Calculate anomaly scores considering the density and distance of the surrounding data • Works well for high-dimensional correlated data with dimensionality reduction technique (PCA, GPLVM, etc.) • No need to assume a distribution and it can be applied even when the density has multimodality Dimensionality reduction LOF anomaly detection
  58. 58. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  59. 59. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Summary • Fully managed IoT ML edge platform • Highly-scalable, easy to process ML leaning and model deployment cycle • Deployment of the algorithms and the models from AWS platform can eliminate the need to go on-site to update the algorithm or the ML model • Lambda function with additional libraries (scikit-learn, numpy, pandas, etc.) can run any ML logic you created. (unless exceeding Lambda size limitation) • Some limitations still exist • It is necessary to consider the fault tolerance at the edge • Greengrass only runs on recent Linux environment • Not all regions support AWS Greengrass yet • Time series analysis needs data cache mechanism on the edge
  60. 60. Thank you! © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. David Nunnerley Masanori Sato Nobutaka Nakazawa
  61. 61. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.

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