Principle:Roboflow Rf detr Model Deployment
| Knowledge Sources | |
|---|---|
| Domains | Deployment, MLOps |
| Last Updated | 2026-02-08 15:00 GMT |
Overview
The process of uploading a trained detection model to the Roboflow platform for serverless inference.
Description
Model deployment packages a trained model's weights and configuration, uploads them to the Roboflow platform, and makes the model available as a serverless API. The deployment process:
- Saves model weights and args to a temporary weights.pt file
- Authenticates with Roboflow using the SDK
- Uploads to a specific project version via version.deploy()
- Cleans up temporary files
Once deployed, the model can be accessed via Roboflow's REST API or through the Inference SDK.
Usage
Use this principle after training to make a model available for production inference without managing infrastructure.
Theoretical Basis
Serverless model deployment abstracts away infrastructure management (GPU provisioning, scaling, load balancing) by providing an API endpoint for inference. The model is stored in Roboflow's model registry and served on-demand.
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