Machine Learning at the Edge

2. MACHINE LEARNING at Amazon Personalized recommendations Inventing entirely new customer experiences Fulfillment automation and inventory management Drones Voice driven interactions

3. The Machine Learning Process Is Hard …

4. Fetch data Clean & format data Prepare & transform data The Machine Learning Process Is Hard …

5. Fetch data Clean & format data Prepare & transform data Train model Evaluate model The Machine Learning Process Is Hard …

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. 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. 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. https://aws.amazon.com/ml-solutions-lab/

10. I N T HE AW S CLO UD Application services Training InferenceTuning Machine Learning in the Cloud

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. 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. Sophisticated models in the cloud Language and speech models Machine Learning in the Cloud And at the EDGE Amazon Echo

14. Sophisticated models in the cloud Vision Models Machine Learning in the Cloud And at the EDGE AWS DeepLens

16. AWS DeepLens

17. AWS DeepLens Project Components

18. Import Model

19. AWS DeepLens Artifacts Model AWS IoT AWS Greengrass Device Stream Project Stream (optional) Scene Device

20. AWS Greengrass Inference Function

21. Get a Frame, Run Inference

22. Inference Output (MQTT)

23. Optimising A Custom Model Optimises a custom model to CI-DNN format so it can run on the GPU

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. AWS DeepLens Artifacts Model AWS IoT AWS Greengrass Device Stream Project Stream (optional) Scene Device

26. AWS DeepLens runs AWS GreenGrass Edge Cloud Machine inference Inference Training

27. AWS DeepLens runs AWS GreenGrass Edge Cloud Machine inference Inference Training

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. Optimising A Custom Model Optimises a custom model to CI-DNN format so it can run on the GPU OpenVINO Toolkit

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

33. Where do I want to process data? CloudEdge

34. Where do I want to process data? Edge Cloud Law of Economics Law of Physics Law of the Land

35. Where do I want to process data? Edge Cloud Law of Economics Law of Physics Law of the Land

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. 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. AWS Greengrass ML Inference Voice/sound recognition Collision avoidance Image recognition Anomaly detection More ! Inference TrainingEdge Cloud Some use cases Machine inference

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. 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. 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. © 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. © 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. Thank you

Machine Learning at the Edge

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Machine Learning at the Edge

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