Ambarella builds computer vision SoCs (system on chips) based on a very efficient AI chip architecture and CVflow that provides the Deep Neural Network (DNN) processing required for edge inferencing use cases like intelligent home monitoring and smart surveillance cameras. Developers convert models trained with frameworks (such as TensorFlow or MXNET) to Ambarella CVflow format to be able to run these models on edge devices. Amazon SageMaker Edge has integrated the Ambarella toolchain into its workflow, allowing you to easily convert and optimize your models for the platform.
In this post, we show how to set up model optimization and conversion with SageMaker Edge, add the model to your edge application, and deploy and test your new model in an Ambarella CV25 device to build a smart surveillance camera application running on the edge.
Smart camera use case
Smart security cameras have use case-specific machine learning (ML) enabled features like

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