7 things to know before using AWS Panorama

Quick dos and don’ts

  • Device only analyzes video streams from IP cameras in the local network
  • Device outputs videos streams from IP cameras with custom visualizations on top
  • Device doesn’t save video streams or images to the cloud unless you implement it in the code
  • Device doesn’t have a local state (e.g. DB) so events can only be recorded via cloud or on-premise server
  • Device can’t be ssh’ed into or remotely entered so you can troubleshoot via telemetry or cloudwatch logs

Application architecture

  • Camera stream
  • Code
  • Model
  • Output
  • Camera stream <> Code
  • Code <> Output

Code artifacts

  • Process each image from the video stream
  • Preprocess image before running the model
  • Running the model on the image
  • Annotating the image before sending it to the output
  • Producing cloudwatch logs and metrics

Model artifacts

Running locally

  • check how your model will be converted to SageMaker Neo (using SageMaker compilation job)
  • check how your graph and code would run on a sample video
  • check how your model would perform on video and how your code would annotate it

Deploying the application

  • have defined manifest so keep it in mind before deployment. You can test your manifest by using the notebooks from the paragraph above.
  • use panorama-cli for building container and packaging application. panorama-cli is a command line interface for managing AWS Panorama application and can be installed via pip (https://pypi.org/project/panoramacli/)

Monitoring

Summary

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Rustem Feyzkhanov

Rustem Feyzkhanov

I'm a senior machine learning engineer at Instrumental, where I work on analytical models for the manufacturing industry, and AWS Machine Learning Hero.