Amazon SageMaker Edge
Easily operate machine learning (ML) models running on edge devices
Amazon SageMaker Edge enables machine learning on edge devices by optimizing, securing, and deploying models to the edge, and then monitoring these models on your fleet of devices, such as smart cameras, robots, and other smart-electronics, to reduce ongoing operational costs. Customers who train models in TensorFlow, MXNet, PyTorch, XGBoost, and TensorFlow Lite can use SageMaker Edge to improve their performance, deploy them on edge devices, and monitor their health throughout their lifecycle.
SageMaker Edge Compiler optimizes the trained model to be executable on an edge device. SageMaker Edge includes an over-the-air (OTA) deployment mechanism that helps you deploy models on the fleet independent of the application or device firmware. SageMaker Edge Agent allows you to run multiple models on the same device. The Agent collects prediction data based on the logic that you control, such as intervals, and uploads it to the cloud so that you can periodically retrain your models over time. SageMaker Edge cryptographically signs your models so you can verify that it was not tampered with as it moves from the cloud to edge devices.
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.
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.
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.
Support Security and Compliance
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 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.