In the rapidly evolving IoT field in manufacturing and transportation, machine learning at the edge gathers insights faster, identifies trends and anomalies, enhancing security, safety, and cost savings; this blog presents options to compile, package, deploy, and run machine learning models across edge devices with Amazon SageMaker, SageMaker Edge Manager Agent, SageMaker Neo Job Compilation with DLR or ONNX runtime.

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<p>The article covers the various options to compile, package, deploy, and run machine learning models across a fleet of devices at the edge using Amazon SageMaker.</p>
<p>Specifically, the article covers:</p>
<ul>
<li>The value of Amazon SageMaker for edge AI</li>
<li>Option 1: Using Amazon SageMaker Edge Manager Agent Service</li>
<li>Option 2: Amazon SageMaker Neo Job Compilation
<ul>
<li>Using Amazon SageMaker Neo with DLR</li>
<li>Using ONNX to export models and inferencing</li>
</ul>
</li>
<li>Conclusion on embracing Amazon SageMaker options for edge AI</li>
</ul>
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Future-proof Your AI at the Edge with AWS

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