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Machine Learning at the Edge

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Machine Learning at the Edge

Join us to see how Public-sector organizations and AWS Partners are combining Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. Applying machine learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Learn how these solutions are architected using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexa.

Join us to see how Public-sector organizations and AWS Partners are combining Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. Applying machine learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Learn how these solutions are architected using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexa.

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Machine Learning at the Edge

  1. 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Craig Lawton, IoT Specialist Architect July 2018 Machine Learning at the Edge ML, IoT and Edge Compute
  2. 2. MACHINE LEARNING at Amazon Personalized recommendations Inventing entirely new customer experiences Fulfillment automation and inventory management Drones Voice driven interactions
  3. 3. The Machine Learning Process Is Hard …
  4. 4. Fetch data Clean & format data Prepare & transform data The Machine Learning Process Is Hard …
  5. 5. Fetch data Clean & format data Prepare & transform data Train model Evaluate model The Machine Learning Process Is Hard …
  6. 6. Fetch data Clean & format data Prepare & transform data Train model Evaluate model Integration with prod Monitor / debug / refresh The Machine Learning Process Is Hard …
  7. 7. A fully managed service that enables data scientists and developers to quickly and easily build machine-learning based models into production smart applications. Amazon SageMaker
  8. 8. Amazon SageMaker Fully managed hosting with auto- scaling One-click deployment Pre-built notebooks for common problems Built-in, high performance algorithms One-click training Hyperparameter optimisation BU IL D TR AIN D EPL O Y DATA!!!
  9. 9. https://aws.amazon.com/ml-solutions-lab/
  10. 10. I N T HE AW S CLO UD Application services Training InferenceTuning Machine Learning in the Cloud
  11. 11. I N T HE AW S CLO UD Inference DE V I C E S & L A M B DA @ E D G E Application services Training InferenceTuning Machine Learning in the Cloud And at the EDGE
  12. 12. I N T HE AW S CLO UD Inference DE V I C E S & L A M B DA @ E D G E Application services Training InferenceTuning Machine Learning in the Cloud And at the EDGE
  13. 13. Sophisticated models in the cloud Language and speech models Machine Learning in the Cloud And at the EDGE Amazon Echo
  14. 14. Sophisticated models in the cloud Vision Models Machine Learning in the Cloud And at the EDGE AWS DeepLens
  15. 15. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
  16. 16. AWS DeepLens
  17. 17. AWS DeepLens Project Components
  18. 18. Import Model
  19. 19. AWS DeepLens Artifacts Model AWS IoT AWS Greengrass Device Stream Project Stream (optional) Scene Device
  20. 20. AWS Greengrass Inference Function
  21. 21. Get a Frame, Run Inference
  22. 22. Inference Output (MQTT)
  23. 23. Optimising A Custom Model Optimises a custom model to CI-DNN format so it can run on the GPU
  24. 24. http://benchmark.ini.rub.de/?section=gtsrb &subsection=dataset • Single-image, multi-class classification problem • More than 40 classes • More than 50,000 images in total • Image sizes vary between 15x15 to 250x250 pixels
  25. 25. AWS DeepLens Artifacts Model AWS IoT AWS Greengrass Device Stream Project Stream (optional) Scene Device
  26. 26. AWS DeepLens runs AWS GreenGrass Edge Cloud Machine inference Inference Training
  27. 27. AWS DeepLens runs AWS GreenGrass Edge Cloud Machine inference Inference Training
  28. 28. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Customer Video / Demo Dynamo6 - Hamilton City Council- TBD
  29. 29. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Local AWS Lambda Running on AWS Greengrass A lambda function running on a Greengrass Core local to the Council network preprocesses the raw video files. Video is optimised and chunked before then being pushed into AWS Kinesis for analysis. AI / ML functions
  30. 30. Optimising A Custom Model Optimises a custom model to CI-DNN format so it can run on the GPU OpenVINO Toolkit
  31. 31. OpenVINO / Intel • Enables CNN-based deep learning inference on the edge • Supports heterogeneous execution across computer vision accelerators—CPU, GPU, Intel® Movidius™ Neural Compute Stick, and FPGA—using a common API • Speeds time to market via a library of functions and preoptimized kernels • Includes optimized calls for OpenCV and OpenVX • IEI* Integration Corporation and AAEON developer kits • https://github.com/intel/Edge-optimized-models • MobileNet 1, MobileNet 5, SqueezeNet 5 for Pedestrians, Cars, Buses, Bicycles and Motorcycles
  32. 32. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  33. 33. Where do I want to process data? CloudEdge
  34. 34. Where do I want to process data? Edge Cloud Law of Economics Law of Physics Law of the Land
  35. 35. Where do I want to process data? Edge Cloud Law of Economics Law of Physics Law of the Land
  36. 36. Where do I want to process data? I n f r a s t r u c t u r e C l o u dP o PI o T E n d p o i n t G a t e w a y A p p l i a n c e C o m m o n P r o g r a m m i n g M o d e l O n b o a r d A W S C l o u d L a m b d a @ E d g e A m a z o n F r e e R T O S G r e e n g r a s s
  37. 37. A W S C l o u d G r e e n g r a s s Where do I want to process data? I n f r a s t r u c t u r e C l o u dP o PI o T E n d p o i n t G a t e w a y A p p l i a n c eO n b o a r d A m a z o n F r e e R T O S L a m b d a @ E d g e
  38. 38. AWS Greengrass ML Inference Voice/sound recognition Collision avoidance Image recognition Anomaly detection More ! Inference TrainingEdge Cloud Some use cases Machine inference
  39. 39. Lots of options… New Features Machine Inference Protocol Adapters Over the Air Updates Local Resource Access Works with Amazon FreeRTOS Broader Ecosystem more distributions preview coming soon New Languages New New
  40. 40. We build IoT solutions through our good friends AWS Partner Ecosystem System Integrators Network Connectivity OEM/ ODM ISVs Silicon / Chipset / Module AWS IoT building blocks Things Cloud Intelligenc e Gateways
  41. 41. What will you build? https://docs.aws.amazon.com/deeplens/latest/dg/deeplens-templated-projects-overview.html https://aws.amazon.com/deeplens/community-projects/
  42. 42. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. National Convention Centre, Canberra 11th September – Training, Workshops, GameDay 12th September – Summit day Keynote featuring Glenn Gore, AWS Head Technologist Helen Souness, CEO RMIT Online Denis Bauer, Team Lead Transformational Bioinformatics, CSIRO
  43. 43. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. We value your feedback! Please share your feedback on the event app or on a paper survey for a chance to win an Amazon Spot
  44. 44. Thank you

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