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AWS DeepLens

  • A deep learning-enabled camera for developers
  • A wireless-enabled camera integrated with AWS Cloud
  • Capable of delivering 100GFLOPS of computing power (1 billion operations per second)
  • Contains sample projects at launch to get you started
  • Optimized for Apache MXNet, TensorFlow, and Caffe
  • Integrates with Amazon Rekognition for advanced image analysis

Common use cases

  • Developing computer vision applications such as:
    • Face Detection
    • Activity Detection
    • Object Detection
    • Bird Classification 
    • Artistic Style Transfer
  • Tutorials dojo strip

AWS DeepLens needs 3 AWS services to create a project:

  • Amazon SageMaker 
    • Train/validate custom or pre-trained models 
  • AWS Lambda
    • Preprocessing 
    • Capturing inference
    • Displaying output
  • AWS IoT Greengrass
    • Deploys application project and Lambda runtime to AWS DeepLens 
    • Handles software and configuration updates

AWS DeepLens Device Library

  • awscam module
      • Runs inference code based on a project’s model.
  • mo module
      • Converts Caffe, Apache MXNet, or TensorFlow deep-learning model artifacts into AWS DeepLens model artifacts.
      • Provides optimizations for AWS DeepLens model artifacts.
  • DeepLens_Kinesis_Video module
    • Can send video feeds from the AWS DeepLens device to Amazon Kinesis Video Streams.

Note: If you are studying for the AWS Certified Machine Learning Specialty exam, we highly recommend that you take our AWS Certified Machine Learning – Specialty Practice Exams and read our Machine Learning Specialty exam study guide.

AWS Certified Machine Learning Specialty Practice Exams


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Written by: El John Acebedo

I'm a Junior AWS Consultant and Content Creator. I taught myself AWS while working as a full-time PCB Design Engineer. I'm interested in making serverless applications on the cloud and have a passion for teaching. I am also currently an SAA (Solutions Architect Associate) certified professional.

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