AI Hub + PySDK Workflow
Use AI Hub to explore hardware, browse public models, and run hosted inference. Then use the same models in your PySDK application when you are ready to build.
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Transform your operations with scalable, cost-efficient AI that runs in the cloud or at the edge. Automate workflows, gain real-time insights, and accelerate innovation across the entire ecosystem.
Uptime SLA
Supported Models
Hardware Runtimes
Use AI Hub to explore hardware, browse public models, and run hosted inference. Then use the same models in your PySDK application when you are ready to build.
Explore integration >
Load a model from a zoo, connect to an inference target, and run your first inference with structured results.
Read the quickstart >Install the package and set up optional components for your runtime and deployment target.
Install PySDK >Enable interactive AI experiences directly in modern browsers without requiring a backend service for every request. This makes it easier to build demos, client-side tools, and privacy-friendly applications with faster user feedback.
Learn more >
Detect and localize faces in images and video streams for analytics, tracking, and vision workflows.
Separate objects or regions at pixel level for scene understanding, masking, and precise analysis.
Identify safety equipment such as helmets, vests, and gloves in industrial and workplace environments.
Assign labels to images for recognition, sorting, and visual categorization tasks.
Explore curated public model zoos with support for common computer vision tasks. Quickly evaluate models, compare outputs, and move from discovery to integration using the same workflow.
Get StartedFind and classify objects within frames for retail, logistics, surveillance, and automation use cases.
Locate license plates in traffic and parking scenarios for vehicle monitoring and access workflows.
Track body keypoints and movement for fitness, ergonomics, behavior analysis, and interaction systems.
Extract printed or structured text from images and documents for search, validation, and automation.
Compile and optimize models in the cloud for supported runtimes, reducing manual setup and accelerating deployment workflows.
Prepare models for different accelerator targets using a unified cloud-based pipeline that simplifies multi-platform execution.
Run compilation workflows in the cloud to support large model libraries, continuous updates, and faster iteration across teams.
Use compiled models across edge devices, browser workflows, and server environments with consistent integration paths.
Access SDKs, model zoo, and all Application Packages — from cloud or edge.