Deploy models at the edge with SageMaker Edge Manager - Amazon SageMaker

Deploy models at the edge with SageMaker Edge Manager


SageMaker Edge Manager is being discontinued on April 26th, 2024. For more information about continuing to deploy your models to edge devices, see SageMaker Edge Manager end of life.

Amazon SageMaker Edge Manager provides model management for edge devices so you can optimize, secure, monitor, and maintain machine learning models on fleets of edge devices such as smart cameras, robots, personal computers, and mobile devices.

Why Use Edge Manager?

Many machine learning (ML) use cases require running ML models on a fleet of edge devices, which allows you to get predictions in real-time, preserves the privacy of the end users, and lowers the cost of network connectivity. With the increasing availability of low-power edge hardware designed for ML, it is now possible to run multiple complex neural network models on edge devices.

However, operating ML models on edge devices is challenging, because devices, unlike cloud instances, have limited compute, memory, and connectivity. After the model is deployed, you need to continuously monitor the models, because model drift can cause the quality of model to decay overtime. Monitoring models across your device fleets is difficult because you need to write custom code to collect data samples from your device and recognize skew in predictions. In addition, models are often hard-coded into the application. To update the model, you must rebuild and update the entire application or device firmware, which can disrupt your operations.

With SageMaker Edge Manager, you can optimize, run, monitor, and update machine learning models across fleets of devices at the edge.

How Does it Work?

At a high level, there are five main components in the SageMaker Edge Manager workflow: compiling models with SageMaker Neo, packaging Neo-compiled models, deploying models to your devices, running models on the SageMaker inference engine (Edge Manager agent), and maintaining models on the devices.

SageMaker Edge Manager uses SageMaker Neo to optimize your models for the target hardware in one click, then to cryptographically sign your models before deployment. Using SageMaker Edge Manager, you can sample model input and output data from edge devices and send it to the cloud for monitoring and analysis, and view a dashboard that tracks and visually reports on the operation of the deployed models within the SageMaker console.

SageMaker Edge Manager extends capabilities that were previously only available in the cloud to the edge, so developers can continuously improve model quality by using Amazon SageMaker Model Monitor for drift detection, then relabel the data with SageMaker Ground Truth and retrain the models in SageMaker.

How Do I Use SageMaker Edge Manager?

If you are a first time user of SageMaker Edge Manager, we recommend that you do the following:

  1. Read the Getting Started section - This section walks you through setting up your first edge packaging job and creating your first fleet.

  2. Explore Edge Manager Jupyter notebook examples - Example notebooks are stored in the amazon-sagemaker-examples GitHub repository in the sagemaker_edge_manager folder.