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Azure IoT Operations with Edge AI in-a-Box

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Introduction

The "AIO with AI in-a-box" project is a comprehensive toolkit designed to help users deploy AI and machine learning solutions at the edge efficiently especially in combination with Azure IoT Operations - AIO. The "AIO with AI in-a-box" includes a modular framework that enables the integration and management of AI models on edge devices (hybride) with AIO, bridging the gap between cloud-based AI models and local OT systems. With a focus on low-latency environments, this allows developers to build and deploy machine learning models directly to hybride edge devices with AIO. This makes it highly relevant for industries looking to integrate AI on the shopfloor, offering scalable, adaptable, and secure AI capabilities in distributed environments.

GlobalSolution

Key Features

This accelerator contains a base Azure IoT Operations/Kubernetes/Azure OpenAI/Azure ML configuration that enables:

  • Azure ML to Azure IoT Operations - Orchestration of resources for the entire Edge AI model lifecycle with AIO (Azure IoT Operations enabled by ARC), including creation, deployment, and proper packaging through Azure ML, Azure IoT Operations and ARC. This involves leveraging key components such as Azure IoT Operations, Arc-Enabled Kubernetes Cluster, Azure ARC, Azure ML, Azure ML Extension and Azure ML CLI V2 notebooks.

  • Azure IoT Operations with Azure Open AI - Deployment and enablement of Cerebral to demonstrate how to leverage Azure OpenAI and Natural Language Processing (NLP) within an Azure IoT Operations environment. In addition, we have included the deployment of a containerized Steamlit application to showcase how to leverage Azure OpenAI from a container/pod running on the Kubernetes cluster.

  • Azure IoT Operations with SLMs (RAG) on Edge Deployment and Enablement of Small Language Models (SLMs) at the Edge within an Azure IoT Operations environment. This setup integrates SLMs to enable Retrieval-Augmented Generation (RAG) at the edge, optimizing performance and reducing reliance on cloud resources for a more efficient and responsive Azure IoT Operations deployment.

Why Azure IoT Operations with Edge AI?

Azure IoT Operations Azure IoT Operations delivers a powerful, scalable solution tailored to the needs of modern manufacturers by combining local processing capabilities with hybrid edge computing. This approach enables real-time data processing directly on the factory floor, facilitating faster, data-driven decision-making where it’s needed most. With built-in support for essential industrial standards like OPC UA and MQTT, Azure IoT Operations seamlessly integrates with diverse manufacturing systems and equipment, ensuring compatibility across various operational environments. Its scalability allows businesses to easily adapt, whether managing a single facility or an extensive network of sites.

The rise of Edge AI amplifies Azure IoT Operations, bringing advanced AI and machine learning models closer to the data source. This shift allows users to harness AI directly at the edge, enabling real-time insights, greater operational efficiency, and reduced latency by processing data locally rather than relying on cloud-based resources alone.

By combining Azure IoT Operations with Edge AI and Generative AI (Gen AI), users can unlock deeper insights into production trends, enabling proactive quality management and advanced analytics. Azure's ecosystem simplifies the deployment of AI models across edge devices and Azure Kubernetes Service (AKS) clusters, enabling scalable, high-performance AI and ML workflows.

This solution not only streamlines deployment but also highlights the seamless integration of Azure ML, Azure OpenAI, and Azure IoT Operations. It provides a clear overview of how these services interconnect, simplifying the understanding of the necessary resources and architecture for building AI-powered workloads. Bringing AI capabilities closer to the Edge empowers users to confidently deploy machine learning models and Gen AI innovations, positioning them to embrace Edge AI with greater agility, efficiency, and assurance

Use cases for this solution

Below are examples of potential use cases that can be built upon this solution framework. Our objective is to help you achieve these real-world applications effectively and efficiently.

Quality control

The solution enhances quality control in manufacturing by enabling monitoring of production parameters using sensor data or camera technolgoy. With advanced AI and machine learning models trained in Microoft Azure, this solution can detect anomalies, such as defects or deviations from quality standards, early in the production process. This proactive approach reduces the risk of defective products reaching customers, thereby minimizing waste and rework.

Energy Management

It can enables manufacturers to implement effective energy management strategies by providing monitoring of energy consumption across production lines and facilities. Through advanced analytics, it can identifies energy inefficiencies, enabling the implementation of corrective actions that reduce waste and optimize energy use. These insights not only lower operational costs but also support ESG (Environmental, Social, and Governance) initiatives by helping manufacturers minimize their carbon footprint.

Faster insights with Factory Copilot

The factory copilot on the Edge can leverages data processing in the factory, providing operators and managers with actionable insights, alerts, and recommendations through a centralized dashboard. This digital assistant can proactively suggest process optimizations, highlight potential issues, and guide operators through complex tasks. With AI-driven analytics and natural language interfaces, the copilot improves situational awareness and empowers factory personnel to make data-driven decisions quickly. This synergy results in smoother operations, reduced downtime, and enhanced productivity across the production line.xfs

Architecture

The core solution runs on an Ubuntu-based virtual machine using K3s. Alternatively, you can deploy this solution to a dedicated hybrid edge device, allowing the machine learning model to operate closer to your equipment.

AIO with AI

AIO with AI

Quick Overview of the Resources to be Deployed with This Template:

  1. You will create all your necessary Azure Resources

    • (K3s Arc-Enabled Cluster, Azure ML Workspace, Container Registry, Storage, Edge VM (for testing), and Azure ML Extension, Azure IoT Operations, Event Hub etc.)
  2. You will levare Azure ML Model Creation/Deployment to a K3s/AIO Cluster Endpoint
    This template provisions the essential Azure Machine Learning resources, the Azure ML Extension, and accompanying notebooks.

    Its primary goal is to guide you through building, training, and registering a model, setting up a Kubernetes endpoint, and deploying the trained model onto a container within your K3s cluster, which supports your Azure IoT Operations environment.

    You will be using the following notebook to facilitate this process:

    1-Img-Classification-Training.ipynb

    Once you have your model, you can deploy it to your AIO K3s Cluster:

    • Build your model into a Docker image and push it to your Kubernetes/AIO endpoint
    • Use the Azure ML Extension to serve as the bridge between Azure ML and your K3s/AIO cluster
  3. You will utilize Cerebral to run GenAI directly at the edge on the K3s/AIO Cluster
    Cerebral will be deployed out of the box with this template

    Please refer to the Cerebral GenAI Repository for reference and troubleshooting

  4. You will use a Streamlit Generative AI application to explore the realm of possibilities
    A Streamlit application will also be deployed, demonstrating how you can operate within an application on the K3s/Azure IoT Operations (AIO) cluster while leveraging Azure OpenAI, LangChain, and a local small language model (SLM). This application serves as a foundational example of how Azure OpenAI can be utilized within a K3s/AIO cluster, providing a flexible starting point for further innovation.

Getting started

The following steps are needed to install this solution:

  1. Review Prerequisites
  2. Deploy the solution to Microsoft Azure
  3. Post deployment
  4. Run and test the solution

Prerequisites

The following prerequisites are needed for a succesfull deployment of this accelator from your own PC.

  • An Azure subscription.

  • Install latest version of Azure CLI

  • Install Azure Developer CLI

  • Enable the Following Resource Providers on your Subscription:

    • Microsoft.AlertsManagement
    • Microsoft.Compute
    • Microsoft.ContainerInstance
    • Microsoft.ContainerService
    • Microsoft.DeviceRegistry
    • Microsoft.EventHub
    • Microsoft.ExtendedLocation
    • Microsoft.IoTOperations
    • Microsoft.IoTOperationsDataProcessor
    • Microsoft.IoTOperationsMQ
    • Microsoft.IoTOperationsOrchestrator
    • Microsoft.KeyVault
    • Microsoft.Kubernetes
    • Microsoft.KubernetesConfiguration
    • Microsoft.ManagedIdentity
    • Microsoft.Network
    • Microsoft.Relay

    (The template will enable all these Resource Providers but its always good to check pre and post deployment.)

  • Install latest version of Bicep

  • Be aware that the template will leverage the following Azure CLI Extensions:

  • Install latest PowerShell version (7.x): Check your current version with $PSVersionTable and then install the latest via "Winget install microsoft.azd"

  • Install VS Code on your machine

  • Ownerships rights on the Microsoft Azure subscription. Ensure that the user or service principal you are using to deploy the accelerator has access to the Graph API in the target tenant. This is necessary because you will need to:

    (Basically you need to deploy the template with a user that has high privelages)

Deployment Flow

Step 1. Clone the Edge-AIO-in-a-Box Repository

Step 2. Create Azure Resources (User Assigned Managed Identity, VNET, Key Vault, EventHub, Ubuntu VM, Azure ML Workspace, Container Registry, etc.)

Sample screenshot of the resources that will be deployed:

AIO with AI Resources

Step 2. SSH onto the Ubuntu VM and execute some kubectl commands to become familiar with the deployed pods and their configurations within the cluster. This hands-on approach will help you understand the operational environment and the resources running in your Kubernetes/AIO setup.

Step 3. Buld ML model into docker image and deploy to your K3s Cluster Endpoint via the Azure ML Extension

Step 4. Push model to Azure Container Registry

Step 5. Deploy model to the Edge via Azure ML Extension to your K3s/AIO Cluster

Deploy to Azure

  1. Clone this repository locally:

    git clone https://github.com/Azure-Samples/edge-aio-in-a-box

  2. Log into your Azure subscription (both are required):

    azd auth login --use-device-code --tenant-id xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxx
    az login --use-device-code --tenant xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxx

  3. Deploy resources:

    azd up

    You will be prompted for a subcription, region and additional parameters:

        adminPasswordOrKey - Pa$$W0rd:)7:)7
    adminUsername - ArcAdmin
    arcK8sClusterName - aioxclusterYOURINITIALS
    authenticationType - password
    location - eastus
    virtualMachineName - aiobxclustervmYOURINITIALS
    virtualMachineSize - Standard_D16s_v4

Post Deployment

Once your resources have been deployed you will need to do the following to get the notebook(s) up running in Azure ML Studio and your Edge Kubernetes Pod to see the solution flows:

  • When running the notebooks in AML your user ([email protected] for instance) won't have permission to alter the storage account or add data to the storage. Please ensure that you have been assigned both Storage Blob Data Reader and Storage Blob Data Contributor roles.

  • Run the Notebook(s)

    • 1-Img-Classification-Training.ipynb
    • This notebook has been automatically uploaded to a folder named EdgeAI within your Azure ML workspace. Its purpose is to guide you through building a custom model in Azure ML, registering the model, and deploying it to a container or endpoint in your Arc-enabled Kubernetes cluster using the Azure ML Extension. Additionally, you can test the endpoint using a Postman collection available in the postman folder within the repository.

  • Testing Locally with PostMan

    • At the end of the notebook, you'll find instructions on how to use Postman to test, ping, and query the K3s/AIO endpoint.
    • The notebook has instructions on how you can setup port forwarding,

    Postman: sklearn mnist model testing through Port Forwarding

    Postman: sklearn mnist model testing through Port Forwarding

    Postman: sklearn mnist model testing through VM Public IP

    Postman: sklearn mnist model testing through VM Public IP

Be Aware

Disclaimer

The lifecycle management (health, kubernetes version upgrades, security updates to nodes, scaling, etc.) of the AKS or Arc enabled Kubernetes cluster is the responsibility of the customer.

All preview features are available on a self-service, opt-in basis and are subject to breaking design and API changes. Previews are provided "as is" and "as available," and they're excluded from the service-level agreements and limited warranty.

Support

We are always looking for feedback on our current experiences and what we should work on next. If there is anything you would like us to prioritize, please feel free to suggest so via our GitHub Issue Tracker. You can submit a bug report, a feature suggestion or participate in discussions.

How to Contribute

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Key Contacts & Contributors

Highlight the main contacts for the project and acknowledge contributors. You can adapt the structure from AI-in-a-Box:

Contact GitHub ID Email
Andrés Padilla @AndresPad [email protected]
Victor Santana @Welasco [email protected]
Chris Ayers @codebytes [email protected]
Neeraj Jhaveri @neerajjhaveri [email protected]
Nabeel Muhammad @nabeelmsft [email protected]
Ali Sanjabi @asanjabi [email protected]
João Carlos Santos @jomacedo [email protected]
Georgina Siggins @georgesiggins [email protected]
Remco Ploeg @rploeg [email protected]
Armando Blanco Garcia @armandoblanco [email protected]
Cheng Chen @ChenCheng368 [email protected]

License

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.