Get started with Machine Learning and Computer Vision Using AWS DeepLens (February 2019)
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The document discusses machine learning and computer vision at the edge using AWS DeepLens. It describes how most machine data cannot reach the cloud due to factors like being in extreme environments or on medical/industrial equipment. It then provides an overview of tools that can help with building datasets, training models in the cloud, and deploying inference at the edge using AWS DeepLens and AWS Greengrass. These tools include Amazon SageMaker for training models and Amazon SageMaker Ground Truth for efficiently annotating datasets.
![© 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T
Deep Learning-based algorithms and pre-trained models
Classification,detection,segmentation
[electric_guitar],
with probability 0.671](https://image.slidesharecdn.com/20190226awssummitberlin-deeplensfinal-converted-190227101806/85/Get-started-with-Machine-Learning-and-Computer-Vision-Using-AWS-DeepLens-February-2019-12-320.jpg)
Get started with Machine Learning and Computer Vision Using AWS DeepLens (February 2019)
- 1. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Get Started with Machine Learning and Computer Vision Using AWS DeepLens Julien Simon Global Evangelist, AI & Machine Learning @julsimon
- 2. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Computing is increasingly available at the edge Machine Learning predictions at the edge would make devices smarter. Could we simply invoke cloud-based models?
- 3. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Most machine data can’t reach the Cloud Medical equipment Industrial machinery Extreme environments
- 4. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Why this problem isn’t going away Law of physics Law of economics Law of the land
- 5. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T To-do list ❑ Build a data set ❑ Experiment with state-of-the art algorithms for computer vision ❑ Train in the Cloud at any scale ❑ Deploy inference code and model at the edge
- 6. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 7. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Annotating large datasets is time-consuming
- 8. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker Ground Truth https://aws.amazon.com/blogs/aws/amazon-sagemaker-ground-truth-build-highly-accurate-datasets-and-reduce-labeling-costs-by-up-to-70 Easily integrate human labelers Get accurate results K E Y F E AT U R E S Automatic labeling via machine learning Ready-made and custom workflows for images and text Label management Quickly label training data Private and public human workforce
- 9. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 10. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Do it yourself or fully-managed: you decide! Amazon EC2 c5 p3 AWS Deep Learning AMIAmazon SageMaker 1 2 3
- 11. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker Build, train,anddeployMLModels atanyscale 1 2 3 17 built-in algorithms, including 3 for computer vision
- 12. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Deep Learning-based algorithms and pre-trained models Classification,detection,segmentation [electric_guitar], with probability 0.671
- 13. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Amazon SageMaker Build, train,anddeployMLModels atanyscale 1 2 3
- 14. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 15. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Deploying at the edge with AWS Greengrass Install the Greengrass runtime Deploy and run Lambda functions Manage from AWS Console Same programming model as in the Cloud Local communication and orchestration
- 16. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Deploying models with AWS Greengrass ML Inference Define models as Greengrass resources and transfer them to your devices Inference takes place on devices Devices take action quickly – even when disconnected
- 17. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Run inference and local actions on device Send insights to the Cloud Generic Deploy model and Lambda function Write inference code Setup Greengrass Architecture Train model
- 18. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T To-do list ✓ Build a data set ✓ Experiment with state-of-the art algorithms for computer vision ✓ Train in the Cloud at any scale ✓ Deploy inference code and model at the edge ❑ Put it all in practice with a fun device
- 19. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 20. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 21. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. AWS DeepLens HD video camera Custom-designed deep learning inference engine Micro-SD Mini-HDMI USB USB Reset Audio out Power HD video camera with on-board compute optimised for deep learning Tutorials, examples, demos, and pre-built models From unboxing to first inference in <10 minutes Integrates with Amazon SageMaker and AWS Lambda 10 MIN
- 22. Get started in minutes with sample projects Detect and recognise objects. OBJECT DETECTION Classify your food. HOT DOG NOT HOT DOG Detect a cat or dog. CAT AND DOG Transfer a style onto video. ARTISTIC STYLE TRANSFER Recognise common activities. ACTIVITY RECOGNITION Detect faces of people. FACE DETECTION
- 23. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T Use your own models with AWS DeepLens • AWS DeepLens can run TensorFlow, Caffe and Apache MXNet models • Inception • MobileNet • NasNet • ResNet • Etc. • Train or fine-tune your model on Amazon SageMaker • Deploy to AWS DeepLens with AWS Greengrass
- 24. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T AWS DeepLens
- 25. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 26. Lambda function: load the model
- 27. Lambda function: optimize a custom model • Custom models need to be optimized for the on-board GPU. • The first call optimizes the model, further calls do nothing.
- 28. Lambda function: get a video frame and predict
- 29. Lambda function: annotate live stream
- 30. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. https://gitlab.com/juliensimon/dlnotebooks/sagemaker/
- 31. © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.S U M M I T ml.aws aws.amazon.com/deeplens aws.training/machinelearning
- 32. Thank you! S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved. Julien Simon Global Evangelist, AI and Machine Learning @julsimon https://medium.com/julsimon
- 33. S U M M I T © 2019, Amazon Web Services, Inc. or its affiliates. All rights reserved.