Amazon SageMaker Edge capabilities

Easily operate machine learning (ML) models running on edge devices

Benefits of SageMaker Edge

Optimize models trained in TensorFlow, PyTorch, XGBoost, and TensorFlow Lite so they can be deployed on any edge device
Deploy models across a fleet of devices independent of firmware and application updates
Continuously improve models with smart data capture for model retraining
Create automated MLOps pipelines for any device fleet, from edge servers to smart cameras and IoT sensors

Amazon SageMaker Edge features

Amazon SageMaker edge capabilities help you optimize, secure, monitor, and maintain ML models across fleets of edge devices.

Create Models

Build and refine models

The SageMaker Edge Agent allows you to capture data and metadata based on triggers that you set so that you can retrain your existing models with real-world data or build new models. Additionally, this data can be used to conduct your own analysis, such as model drift analysis.

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Choice of deployment methods

We offer three options for deployment. GGv2 (~ size 100MB) is a fully integrated AWS IoT deployment mechanism. For those customers with a limited device capacity, we have a smaller built-in deployment mechanism within SageMaker edge capabilities. For customers who have a preferred deployment mechanism, we support third party mechanisms that can be plugged into our user flow.

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Optimize ML models

Amazon SageMaker Edge Compiler automatically optimizes ML models for deployment on a wide variety of edge devices. SageMaker Edge Compiler compiles your trained model into an executable format that applies performance optimizations that can make your model run up to 25x faster on the target hardware.

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