Summary
Building vision AI applications at the edge can be challenging due to long, complex development cycles. NVIDIA Metropolis microservices provide powerful, customizable, cloud-native APIs and microservices to develop vision AI applications and solutions. This article explores how NVIDIA Metropolis microservices can help developers build and deploy vision AI applications at the edge, leveraging the NVIDIA Jetson edge-AI platform.
Building Vision AI Applications at the Edge
Vision AI applications are becoming increasingly important across various industries, from retail stores and warehouses to airports and roadways. These applications require real-time processing, cloud connectivity, and the ability to deliver operational data. However, building such applications can be challenging due to the complexity of software and hardware capabilities required.
The Challenge of Building Vision AI Applications
Building vision AI applications involves bringing together a wide range of software and hardware capabilities. This includes arrays of sensors, edge computing, and dozens of software functions and components running simultaneously. Traditional development methods often result in long, complex development cycles, making it difficult to bring sophisticated cloud-connected edge AI products to market quickly.
NVIDIA Metropolis Microservices
NVIDIA Metropolis microservices provide a solution to this challenge. These microservices are powerful, customizable, and cloud-native, allowing developers to build and deploy vision AI applications at the edge. The framework includes NVIDIA Jetson, enabling developers to quickly build and productize performant and mature vision AI applications.
Key Features of NVIDIA Metropolis Microservices
- Cloud-Native: Built to run on NVIDIA Cloud and data center GPUs, along with the NVIDIA Jetson edge AI platform.
- Customizable: Provides a growing suite of options that include video storage and management, AI inference pipelines, and analytics.
- Reference Applications: Includes reference applications that address a variety of challenging vision AI use-cases, such as multi-camera tracking, occupancy heatmaps, AI-powered NVRs, and more.
- Platform Services: Offers platform services such as system monitoring, API gateway, cloud-connectivity, and more, providing faster time to production.
Benefits of Using NVIDIA Metropolis Microservices
Using NVIDIA Metropolis microservices can significantly reduce development time and enhance the efficiency of vision AI applications. Here are some key benefits:
- Faster Development: Reduces development time by providing pre-built microservices and reference applications.
- Enhanced Resilience and Security: Offers enhanced resilience and security features, making it easier to deploy and manage vision AI applications.
- Scalability: Supports scaling deployments from edge to cloud, making it easier to handle large volumes of data.
Real-World Applications
NVIDIA Metropolis microservices can be used in a variety of real-world applications, including:
- Smart Spaces: AI-powered computer vision can streamline processes and better automate physical infrastructure in smart spaces.
- Retail and Warehouses: Vision AI applications can help manage inventory, track customer behavior, and improve security.
- Airports and Roadways: Vision AI can be used for traffic management, surveillance, and security applications.
How to Get Started
To get started with NVIDIA Metropolis microservices, developers can:
- Register for the NVIDIA Developer Program: Access resources, tools, and support for building vision AI applications.
- Explore Reference Applications: Use pre-built reference applications to speed up development.
- Leverage NVIDIA Jetson: Build and deploy vision AI applications on the NVIDIA Jetson edge AI platform.
Table: Key Features of NVIDIA Metropolis Microservices
| Feature | Description |
|---|---|
| Cloud-Native | Built to run on NVIDIA Cloud and data center GPUs, along with the NVIDIA Jetson edge AI platform. |
| Customizable | Provides a growing suite of options that include video storage and management, AI inference pipelines, and analytics. |
| Reference Applications | Includes reference applications that address a variety of challenging vision AI use-cases. |
| Platform Services | Offers platform services such as system monitoring, API gateway, cloud-connectivity, and more. |
Table: Benefits of Using NVIDIA Metropolis Microservices
| Benefit | Description |
|---|---|
| Faster Development | Reduces development time by providing pre-built microservices and reference applications. |
| Enhanced Resilience and Security | Offers enhanced resilience and security features, making it easier to deploy and manage vision AI applications. |
| Scalability | Supports scaling deployments from edge to cloud, making it easier to handle large volumes of data. |
Conclusion
Building vision AI applications at the edge can be challenging, but NVIDIA Metropolis microservices provide a powerful solution. By leveraging these microservices, developers can reduce development time, enhance resilience and security, and scale deployments from edge to cloud. Whether it’s smart spaces, retail and warehouses, or airports and roadways, NVIDIA Metropolis microservices can help bring sophisticated vision AI applications to market faster.