NVIDIA Metropolis is an edge-to-cloud platform designed to enable the development and deployment of AI-powered applications for video analytics and smart cities. It provides a suite of tools, libraries, and SDKs that allow developers to build and deploy real-time video analytics solutions for various use cases, such as traffic management, public safety, retail analytics, and industrial automation.
Key components and features of NVIDIA Metropolis include:
Deep Learning Acceleration: NVIDIA GPUs (Graphics Processing Units) are optimized for deep learning inference, enabling efficient processing of large volumes of video data for real-time analytics tasks, such as object detection, classification, and tracking.
Video Ingestion: Tools and APIs for capturing, streaming, and ingesting video data from a variety of sources, including IP cameras, drones, sensors, and IoT devices, into the Metropolis platform for analysis.
Pre-trained Models: NVIDIA provides pre-trained deep learning models and frameworks, such as TensorFlow and PyTorch, optimized for video analytics tasks, allowing developers to quickly deploy and customize AI models for their specific use cases.
Edge Computing: NVIDIA Jetson edge AI platforms enable inference at the edge of the network, allowing for real-time processing and analysis of video data directly on edge devices, reducing latency and bandwidth requirements.
Cloud Integration: Integration with cloud services, such as NVIDIA Clara AI toolkit and NVIDIA EGX Edge AI platform, allows for seamless deployment of AI models, management of edge devices, and scaling of resources across distributed environments.
Developer Tools: NVIDIA provides a range of developer tools, SDKs, and APIs, including NVIDIA DeepStream SDK for building video analytics applications, NVIDIA TensorRT for optimizing deep learning models for inference, and NVIDIA Transfer Learning Toolkit for fine-tuning pre-trained models for specific tasks.
Chairman of the Board - LatinRisk Argentina
1moHarold Sinnott thank you for @me!!!