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機器學習技術在工業應用上的最佳實務

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機器學習技術在工業應用上的最佳實務

  1. 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Machine Learning at the Edge for Industrial Applications Richard Elberger Global Partner Solutions Architect, IoT Amazon Web Services, Partner Network
  2. 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda Tenets Industrial IoT architecture AIoT lifecycle – a four-part story
  3. 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  4. 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Effectively maintain the system over its lifecycle
  5. 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Exploit system cost effectiveness via new intelligence
  6. 6. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Better decisions through central dashboards and monitoring
  7. 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Derive value through new capabilities
  8. 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS IoT Greengrass Amazon FreeRTOS Amazon FreeRTOS Amazon FreeRTOS Amazon FreeRTOS Amazon FreeRTOS
  9. 9. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  10. 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Industrial Control Fieldbus
  11. 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  12. 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AIoT – Machine Learning at the Edge Data transport and routing Data aggregation, enrichment, cleansing, time series, and model config Machine learning and model generation Data collection and model inference Intelligence and outcomes Train in the cloud and infer at the edge AWS IoT Core AWS Snowball Amazon Kinesis AWS IoT Analytics Amazon EMR Amazon S3 Amazon SageMaker Amazon EC2 Amazon SageMaker Ground Truth Apache MXNet on AWS AWS Deep Learning AMIs AWS Snowmobile AWS IoT Greengrass Amazon FreeRTOS AWS IoT SiteWise bespoke applications
  13. 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AIoT – Machine Learning at the Edge Data transport and routing Data aggregation, enrichment, cleansing, time series, and model config Machine learning and model generation Data collection and model inference Intelligence and outcomes Train in the cloud and infer at the edge AWS IoT Core AWS Snowball Amazon Kinesis AWS IoT Analytics Amazon EMR Amazon S3 Amazon SageMaker Amazon EC2 Amazon SageMaker Ground Truth XILINX DNNDK AWS Snowmobile AWS IoT Greengrass UltraScale+ (DPU) Amazon FreeRTOS Zynq7000 (DPU)
  14. 14. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  15. 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AIoT – Machine Learning at the Edge Data transport and routing Data aggregation, enrichment, cleansing, time series, and model config Machine learning and model generation Data collection and model inference Intelligence and outcomes Train in the cloud and infer at the edge AWS IoT Core AWS Snowball Amazon Kinesis AWS IoT Analytics Amazon EMR Amazon S3 Amazon SageMaker Amazon EC2 Amazon SageMaker Ground Truth Apache MXNet on AWS AWS Deep Learning AMIs AWS Snowmobile AWS IoT Greengrass Amazon FreeRTOS AWS IoT SiteWise bespoke applications
  16. 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. In the beginning
  17. 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Judgment on how to bring in data
  18. 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  19. 19. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AIoT – Machine Learning at the Edge Data transport and routing Data aggregation, enrichment, cleansing, time series, and model config Machine learning and model generation Data collection and model inference Intelligence and outcomes Train in the cloud and infer at the edge AWS IoT Core AWS Snowball Amazon Kinesis AWS IoT Analytics Amazon EMR Amazon S3 Amazon SageMaker Amazon EC2 Amazon SageMaker Ground Truth Apache MXNet on AWS AWS Deep Learning AMIs AWS Snowmobile AWS IoT Greengrass Amazon FreeRTOS AWS IoT SiteWise bespoke applications
  20. 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Ingesting and methodically curating
  21. 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Iteratively evaluating and refining Where data science meets art annotation cleansing data types re-process raw data
  22. 22. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  23. 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AIoT – Machine Learning at the Edge Data transport and routing Data aggregation, enrichment, cleansing, time series, and model config Machine learning and model generation Data collection and model inference Intelligence and outcomes Train in the cloud and infer at the edge AWS IoT Core AWS Snowball Amazon Kinesis AWS IoT Analytics Amazon EMR Amazon S3 Amazon SageMaker Amazon EC2 Amazon SageMaker Ground Truth Apache MXNet on AWS AWS Deep Learning AMIs AWS Snowmobile AWS IoT Greengrass Amazon FreeRTOS AWS IoT SiteWise bespoke applications
  24. 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. FRAMEWORKS ML Frameworks + Infrastructure ML Services AI Services INTERFACES INFRASTRUCTURE Amazon SageMaker Amazon Transcribe Amazon Polly Amazon Lex CHATBOTS Amazon Rekognition Image Amazon Rekognition Video VISION SPEECH Amazon Comprehend Amazon Translate LANGUAGES P3 P3dn C5 C5n Elastic inference Inferentia AWS Greengrass Ground Truth Notebooks Algorithms + Marketplace RL Training Optimization Deployment Hosting AWS Confidential - Do not Distribute
  25. 25. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Deep learning frameworks and toolchains
  26. 26. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Feeding the art: data sets
  27. 27. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Training job: compilation
  28. 28. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Staging
  29. 29. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  30. 30. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. AIoT – Machine Learning at the Edge Data transport and routing Data aggregation, enrichment, cleansing, time series, and model config Machine learning and model generation Data collection and model inference Intelligence and outcomes Train in the cloud and infer at the edge AWS IoT Core AWS Snowball Amazon Kinesis AWS IoT Analytics Amazon EMR Amazon S3 Amazon SageMaker Amazon EC2 Amazon SageMaker Ground Truth Apache MXNet on AWS AWS Deep Learning AMIs AWS Snowmobile AWS IoT Greengrass Amazon FreeRTOS AWS IoT SiteWise bespoke applications
  31. 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Mixed criticality system – block diagram
  32. 32. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Mixed criticality system – software placement PS PL AWS IoT Greengrass
  33. 33. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Mixed criticality system – pin wiring PS PL
  34. 34. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Updating the model
  35. 35. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. InferenceHandler m2m PS PL AWS IoT Greengrass
  36. 36. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Device level dependencies /dev/uio0 /dev/uio1 /dev/i2c-0 /dev/i2c-1 /dev/mem InferenceHandler m2m PS PL AWS IoT Greengrass
  37. 37. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Reporting inference results ImageUploadHandler m2m PS PL AWS IoT Greengrass
  38. 38. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Staging data for building up the data lake ImageStageHandler m2m PS PL AWS IoT Greengrass
  39. 39. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  40. 40. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Recap Data transport and routing Data aggregation, enrichment, cleansing, time series, and model config Machine learning and model generation Data collection and model inference Intelligence and outcomes AWS IoT Core AWS Snowball Amazon Kinesis AWS IoT Analytics Amazon EMR Amazon S3 Amazon SageMaker Amazon EC2 Amazon SageMaker Ground Truth Apache MXNet on AWS AWS Deep Learning AMIs AWS Snowmobile AWS IoT Greengrass Amazon FreeRTOS AWS IoT SiteWise bespoke applications
  41. 41. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  42. 42. © 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. AWS DeepLens The world’s first deep learning- enabled video camera for developers • A new way to learn ML though sample projects, with practical, hands-on examples • Run deep learning models locally on the camera to recognize or classify without streaming to the cloud • Easy to customize and fully programmable using AWS Lambda • Integrated with Amazon SageMaker for custom model deployment • Runs on any deep learning framework, including Apache MXNet, TensorFlow, and Caffe. Available now on amazon.com for $249
  43. 43. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T GET STARTED WITH SAMPLE PROJECTS ARTISTIC STYLE TRANSFER OBJECT DETECTION FACE DETECTIONHOT DOG / NOT HOT DOG CAT VS. DOG ACTIVITY DETECTION Or build custom deep learning models in the cloud using Amazon SageMaker HEAD POSE DETECTION
  44. 44. © 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. • Intel Atom® Processor • Gen9 graphics • Ubuntu OS- 16.04 LTS • 100 GFLOPS performance • Dual band Wi-Fi • 8 GB RAM • 16 GB storage (eMMC) • 32 GB SD card • 4 MP camera with MJPEG • H.264 encoding at 1080p resolution • 2 USB ports • Micro HDMI • Audio out • AWS Greengrass preconfigured • clDNN-optimized for MXNet • Key Differentiators/Technologies • Intel cLDNN Library optimized for MXNet • Intel Deep Learning Deployment Toolkit AWS DeepLens Specifications
  45. 45. © 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. AWS DEEPLENS ARCHITECTURE Video out Data out I N F E R E N C E D E P L O Y P R O J E C T S Manage device Security Console Project Management AWS Cloud Intel: Model Optimizer cIDNN and Driver
  46. 46. Thank you! © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Richard Elberger Global Partner Solutions Architect - IoT https://github.com/rpcme/ https://www.linkedin.com/in/richardelberger/ https://twitter.com/richardelberger

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