# Overview Qualcomm’s containerized microservices architecture offers numerous advantages to significantly enhance the scalability, flexibility, and maintainability of your software. By breaking solutions into smaller, independent services developers can achieve greater modularity and ease of deployment.
Last Published: May 09, 2025
Additional benefits - **Flexibility**: Different technologies and languages can be used for different services - **Fault isolation**: Issues on one service don’t necessarily impact the entire system - **Faster deployment**: Smaller, independent services can be developed, deployed, and tested more efficiently - **Portability**: Containers ease deployment of microservices to a variety of Qualcomm chipsets 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) ## Key concepts Extensible microservices Qualcomm’s edge computing platform leverages a suite of microservices designed to provide flexibility and scalability for various applications. You can use these microservices as-is or extend them to meet your needs. AI inference microservice The core of the platform is an AI inference microservice built on the Qualcomm Intelligent Multimedia SDK. Using GStreamer, this SDK leverages platform hardware acceleration and can handle a wide range of AI models and audio/video pipelines. It provides a robust framework to deploy AI inference tasks at the edge, ensuring low latency and high performance. Communication between microservices Microservices on the device communicate with each other using Redis. Redis serves as a fast, in-memory data store that facilitates efficient message passing and data sharing between microservices. This architecture allows seamless integration and coordination between system components, such as sending inference data from the AI microservice to an analytics microservices for further processing. Extensibility You can extend these microservices to add custom functionality or to integrate with other systems. The modular design of these microservices ensures that they can be adapted to different use cases, providing a flexible foundation for edge computing applications. ## Reference solutions In addition to these individual microservices, Qualcomm provides reference solutions built using these microservices. These reference solutions are designed to demonstrate the capabilities of the platform and to serve as starting points for developers. Ready-to-use solutions These reference solutions are fully functional out-of-the-box, allowing developers to efficiently deploy and test them in their environments. These solutions demonstrate common use cases and best practices for integrating and utilizing Qualcomm microservices. Extensible framework Each reference solution is designed with extensibility in mind. Developers can customize and extend these solutions to fit their specific requirements. These reference solutions provide a flexible foundation for further development, including adding new features, integrating with other systems, or optimizing performance. ### Available reference solutions These reference solutions help you understand how to use these microservices effectively and accelerate development of custom applications tailored to your unique needs. #### Personal protective equipment (PPE) detection Ensures worker safety by monitoring compliance with prescribed safety gear, such as hard hats and safety vests. This solution leverages advanced AI models to detect, in real-time, whether workers are wearing the required PPE. Key features - Real-time detection: Utilizes AI-powered image recognition to monitor and verify the use of PPE on-site. - Configurable alerts: Clients can configure various alert triggers via HTTP, enabling customized notifications based on specific safety requirements. - Alert querying: Provides an HTTP interface to query alerts, enabling clients to review and manage safety compliance data efficiently. #### Restricted zone (RZ) detection The restricted zone solution is designed to enhance security by monitoring and controlling access to sensitive or restricted areas. This solution uses advanced computer vision and machine learning techniques to identify unauthorized entry and ensure that only authorized personnel can access these zones. Key features - Configurable zones of interest: Clients can define multiple zones of interest for each camera feed through an HTTP interface. - AI-based region detection: Advanced AI models accurately detect individuals inside or outside the configured zones in real-time video streams. - Flexible alert configuration: Clients can set up alerts based on the minimum or maximum number of people detected within or outside the specified zones of interest. Last Published: May 09, 2025 [Next Topic Getting Started: Running AI Workflow Samples](https://docs.qualcomm.com/bundle/publicresource/80-84692-1/topics/getting-started.md)