Deploy ML models to Arm edge devices using Edge Impulse and AWS IoT Greengrass
Introduction
Understand the Edge Impulse and AWS IoT Greengrass deployment architecture
Select and set up your edge device
Set up your Edge Impulse project
Install AWS IoT Greengrass on your Arm edge device
Store your Edge Impulse API key in AWS Secrets Manager
Create the Edge Impulse Greengrass component
Deploy the component to your edge device
Verify inference and view results
Next Steps
Deploy ML models to Arm edge devices using Edge Impulse and AWS IoT Greengrass
Introduction
Understand the Edge Impulse and AWS IoT Greengrass deployment architecture
Select and set up your edge device
Set up your Edge Impulse project
Install AWS IoT Greengrass on your Arm edge device
Store your Edge Impulse API key in AWS Secrets Manager
Create the Edge Impulse Greengrass component
Deploy the component to your edge device
Verify inference and view results
Next Steps
Who is this for?
This Learning Path is for embedded and IoT engineers who want to deploy Edge Impulse ML models to Arm-based edge devices at scale using AWS IoT Greengrass.
What will you learn?
Upon completion of this Learning Path, you will be able to:
- Set up an Arm-based edge device for ML inference with Edge Impulse.
- Install and configure AWS IoT Greengrass on the edge device.
- Deploy an Edge Impulse ML model as a Greengrass custom component.
- Verify model inference results through AWS IoT Core.
Prerequisites
Before starting, you will need the following:
- An Edge Impulse Studio account
- An AWS account with administrator access
- A supported Arm-based edge device such as a Raspberry Pi 5, NVIDIA Jetson, Qualcomm Dragonwing QC6490, or an Arm-based Amazon EC2 instance
- An SSH client and familiarity with the Linux command line
- Basic understanding of ML concepts
Summary
This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
Frequently asked questions
These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.
EI_API_KEY. Set its key to ei_api_key.