Edge Deployment

AI capabilities that run directly on local devices, reducing reliance on remote servers and enabling lower-latency, privacy-focused inference.

A deployment model where application code runs in ephemeral containers managed automatically by a cloud provider.

The process of making an AI model available for inference by deploying it behind an interface that accepts requests and returns predictions or generated outputs.

The underlying hardware and software infrastructure where applications or models execute.

The practice of deploying AI models or applications on graphics processing units (GPUs) to accelerate inference or training by leveraging massively parallel computation for high-performance workloads.