Amazon SageMaker Edge

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

Optimize models trained in TensorFlow, MXNet, 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

Build and refine models for increased accuracy over time

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.

SageMaker Edge - Capturing data from your edge device - Amazon Web Services (6:24)

Your 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. For customers who have a preferred deployment mechanism, we support third party mechanisms that can be plugged into our user flow.

SageMaker Edge - Quick device setup demo - Amazon Web Services (8:21)

Visual dashboard to monitor your fleet of devices

Amazon SageMaker Edge Manager provides a dashboard so you can understand the performance of models running on each device across your fleet. The dashboard helps you visually understand overall fleet health and identify the problematic models through a dashboard in the console. When a problem is identified, you can collect model data, relabel the data, retrain the model, and redeploy the model.

Optimize ML models for a wide range of devices

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.

SageMaker Edge - Compilation demo - Amazon Web Services (5:49)

Support Security and Compliance

Amazon SageMaker Edge packages the ML model by signing it with customer-supplied keys or AWS keys. The Edge Agent authenticates the signature and also verifies that the model has not been tampered with before loading the model into the runtime.
 
SageMaker Edge - Packaging a model demo - Amazon Web Services (4:24)

Customers

Levnovo customer logo

Lenovo™, the #1 global PC maker, recently incorporated Amazon SageMaker into its latest predictive maintenance offering.  

"The new SageMaker Edge Manager will help eliminate the manual effort required to optimize, monitor, and continuously improve the models after deployment. With it, we expect our models will run faster and consume less memory than with other comparable machine-learning platforms. SageMaker Edge Manager allows us to automatically sample data at the edge, send it securely to the cloud, and monitor the quality of each model on each device continuously after deployment. This enables us to remotely monitor, improve, and update the models on our edge devices around the world and at the same time saves us and our customers' time and costs."

Igor Bergman, Lenovo Vice President, Cloud & Software of PCs and Smart Devices.

Basler customer logo

Basler AG is a leading manufacturer of high-quality digital cameras and accessories for industry, medicine, transportation and a variety of other markets.

“Basler AG delivers intelligent computer vision solutions in a variety of industries, including manufacturing, medical, and retail applications. We are excited to extend our software offering with new features made possible by Amazon SageMaker Edge Manager. To ensure our machine learning solutions are performant and reliable, we need a scalable edge to cloud MLOps tool that allows us to continuously monitor, maintain, and improve machine learning models on edge devices. SageMaker Edge Manager allows us to automatically sample data at the edge, send it securely to the cloud, and monitor the quality of each model on each device continuously after deployment. This enables us to remotely monitor, improve, and update the models on our edge devices around the world and at the same time saves us and our customers' time and costs."

Mark Hebbel, Head of Software Solutions at Basler.

Resources

BLOG

Build ML models at the edge

Blog

Operate ML models on the edge

workshop

SageMaker Edge Manager workshop

video

MLOps for edge devices with Amazon SageMaker Edge Manager

What's new

Date (Newest to Oldest)
  • Date (Newest to Oldest)
1
No results found