In this solution, an IoT Edge module is used to download an AI model and then enable machine learning inference. You can use pre-trained LiteRT or ONNX models in this solution.

This repository contains examples and best practices for deploying machine learning (ML) models on Azure IoT Edge, provided as Jupyter notebooks. This topic leverages Artificial Intelligence (AI), Internet of Things (IoT), and Azure IoT Edge.

Jan 4, 2019 · In this article, I’ll explain two ways you can design ML on the edge. Specifically, I’ll describe how to write a custom model and IO function for a device, and how to use the Microsoft Embedded Learning Library (ELL) set of tools to deploy an optimized ML model to a …

Feb 8, 2019 · Azure IoT Edge is a platform that’s remotely managed from an IoT Hub. It enables analysis of data on devices instead of in the cloud by moving parts of your workload to the edge. Your devices can spend less time sending messages to the cloud, and they can react autonomously and quickly to changes.

Oct 4, 2024 · From preparing ML models for optimized ML@Edge operation to leveraging Azure IoT Edge for seamless deployment and management, these sections intricately explore techniques, best practices, and the Azure-specific details essential for successful implementation.

Oct 1, 2020 · Today, we are introducing a reference implementation for a CI/CD pipeline built using Azure DevOps to train a CNN model, package the model in a docker image and deploy to a remote device using Azure IoT Edge for ML inference on the edge device.

Oct 17, 2023 · Azure IoT Edge allows ML models to be deployed as modules on edge devices, enabling real-time inference and decision-making without relying on cloud connectivity. It supports popular ML frameworks such as TensorFlow and ONNX, allowing users to train models in the cloud and deploy them at the edge.

Training a machine learning model in the cloud with data collected from IoT devices, and deploy the model to IoT Edge. Frequently, IoT applications want to take advantage of the intelligent cloud and the intelligent edge.

In this article, we provide ML-MCU, a framework with our novel Optimized-Stochastic Gradient Descent (Opt-SGD) and Optimized One-Versus-One (Opt-OVO) algorithms to enable both binary and multiclass ML classifier training directly on MCUs.

In this project, we'll demonstrate an MLOps pipeline consisting of Edge Impulse and Microsoft Azure IoT Edge, to build a scalable, enterprise-grade edge AI deployment.