Start fast
Get up and running in minutes, regardless of your current skill level with vision machine learning. Connect your camera to Azure IoT Hub that controls the network traffic between the device and the cloud, and see the camera in action by running a default Vision AI module that recognizes 183 different objects.
Build fast
- New to Vision ML? Start building a vision model by uploading and tagging pictures, letting Azure Custom Vision Service do the heavy lifting.
- Experienced with vision ML? Use Jupyter notebooks and Visual Studio Code to create and train custom vision models using Azure Machine Learning (AML). AML services enable you to prepare data and train models. You can then convert the trained model to the custom DLC format and package it into an IoT Edge module to deploy to the Vision AI Dev Kit.
Deploy Fast
Azure IoT Hub can push your containerized vision ML models and other modules to the Vision AI DevKit with ease, whether the camera is on your desk or in another country.
Join the Community
Get help and help others with vision ML projects by joining our Tech Community and Gitter.
Build the intelligent edge
As an Intelligent Edge device, the Vision AI DevKit does inferences and runs containerized Azure services locally in the device. Moving these workloads to the edge of the network means vision ML inferencing work requires less cloud interaction while also enabling quick reaction to local events, allowing operation during extended offline periods.
Get Support
Get quick answers to your questions by asking them here.
If you purchased your camera from Arrow, you are also entitled to one hour of complimentary support from eInfochips. This support includes:
- Quick start guidance for connectivity to Azure IoT Hub
- Guidance to create and deploy a custom vision AI model using custom vision services
- Guidance for setting up Visual Studio Code environment for Vision ML kit
- Setup for Azure account (if not available)
- Guidance to change personal connectivity on VAIDK i.e. Wi-Fi access / passphrase changes
- Guidance for upgrade and install latest firmware build for VAIDK, followed by reboot procedure
- Factory default settings and reboot procedures.
To request support from eInfoChips, go here.
What's New?
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Partnering with Purdue
MS partners with Purdue University to publish a project on the Workplace Safety (PPE) model using the Vision AI Dev Kit and Custom Vision. Read the published article here |
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Deploying Models
Mahesh is back to show us how you can train and deploy new AI models on the device in a matter of minutes. Watch on Channel 9 |
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Using Audio
Learn how to use audio from the Vision AI DevKit as input data for IoT solutions. Watch on Channel 9 | |
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Unboxing!
See how easy it is to set up the Vision AI Developer Kit and connect it to Azure services! Watch on Channel 9 | |
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Visit the AI@Edge portal!
Microsoft is launching an AI@Edge community. Find hardware, ML and cloud resources you need to create solutions using intelligence at the edge |
An Azure IoT starter kit, the Vision AI DevKit can be used with models built and trained using the Azure Machine Learning service and CustomVision.ai.
The Vision AI DevKit features the Qualcomm Visual Intelligence Platform for hardware acceleration of AI models to deliver superior inferencing performance.
eInfoChips (an Arrow Company) is a leading provider of design services in vision based AI and the Edge2Cloud services. With 24+ years of full spectrum product engineering expertise and a 2000+ member team skilled at engineering products across Silicon – Hardware – Software – Cloud spectrum, helping companies realize their connected product roadmaps in partnership with Qualcomm and Microsoft.
