Computer vision is becoming increasingly important in industrial applications providing product line management, inventory control, and safety monitoring capabilities. However, using computer vision at the edge of the network poses challenges, especially around latency and reliance on mixed network and cloud resources. To address this, Microsoft CEO Satya Nadella introduced the concept of the “intelligent edge,” which provides cloud-native tools and services to devices within the network.
Microsoft provides tools to containerize and deliver Azure Cognitive Services via Azure IoT Edge, but there remains a need for a solution for custom edge implementations. Containers have emerged as the ideal deployment method for edge software, and Kubernetes and service meshes provide an agnostic platform for code deployment. In this context, the KAN (KubeAI Application Nexus) project was created as an open source solution hosted on GitHub.
KAN aims to simplify the development and management of machine learning applications on Kubernetes at scale. It runs code on edge hardware, aggregates data from locally connected devices, and leverages pre-trained machine learning models to deliver insights. KAN also provides a monitoring and management portal and low-code development environment for his Kubernetes systems, either on-premises or cloud-based.
Specifically, the KAN management portal acts as a control and monitoring interface rather than as a data endpoint. Tighter integration is provided when integrated with Azure Edge and AI services such as Azure IoT Hub and Azure Cognitive Services and hosted in Azure. To get started with KAN, you need a Kubernetes cluster with Helm support. Azure users can take advantage of Azure Kubernetes Service (AKS) to simplify setup.
Once KAN is installed, users can connect computing devices such as NVIDIA Edge hardware and Azure Stack Edge to build applications on the KAN portal. KAN supports a variety of devices running on top of Kubernetes clusters or Azure Edge devices. The platform also facilitates testing using Azure VMs as test devices to create digital twins to ensure edge systems are performing as expected. Industrial IP cameras are supported, and KAN enables many-to-many processing, allowing multiple applications to interact with camera feeds.
Building a machine learning application with KAN involves choosing a device architecture and acceleration technology. KAN recommends using accelerated devices such as NVIDIA and Intel GPUs and NPUs for safety-critical edge applications. KAN provides a node-based graphic design tool for connecting camera inputs to models and building “AI skills” to transform/filter outputs. Data can be exported to other applications and services to enable customized workflows.
Once an application is built and tested, KAN simplifies packaging and deployment to target devices through a portal. Currently, KAN is limited to deploying to his one device at a time, but we aim to support deployment to multiple devices in the future. This simplifies the delivery of machine learning applications to his Kubernetes system or Microsoft’s Azure IoT Edge runtime container hosts, providing a centralized view of all deployments.
Inspired by the canceled Azure Percept solution, KAN aims to simplify edge AI deployments using low-code tools. Taking a similar approach to the Percept developer experience, KAN combines the concepts of his IoT tools with the capabilities of Microsoft’s Power Platform to enhance the ease of building and deploying machine learning applications.
In conclusion, KAN streamlines the development and deployment of machine learning applications for computer vision at the network edge. Focused on Kubernetes and its support for various computing devices, his KAN provides a platform for experimental and large-scale edge AI implementations. By simplifying the process, KAN opens up the possibility of solving problems efficiently and effectively through edge machine learning.
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Niharika is a technical consulting intern at Marktechpost. She is in her third year of undergraduate studies and is currently completing her Bachelor’s degree at the Indian Institute of Technology (IIT), Kharagpur. She is a very passionate person who has a keen interest in machine learning, data her science, AI and avid reader of the latest developments in these fields.
