| Roboflow Inference enables you to deploy computer vision models faster than ever. | |
| Here is an example of a model running on a video using Inference ([See the code](https://github.com/roboflow/inference/blob/main/examples/inference-client/video.py)): | |
| <video width="100%" autoplay loop muted> | |
| <source src="https://media.roboflow.com/football-video.mp4" type="video/mp4"> | |
| </video> | |
| Before Inference, deploying models on device involved: | |
| 1. Writing custom inference logic, which often requires machine learning knowledge. | |
| 2. Managing dependencies. | |
| 3. Optimizing for performance and memory usage. | |
| 4. Writing tests to ensure your inference logic worked. | |
| 5. Writing custom interfaces to run your model over webcams and streams, if you were deploying live. | |
| Inference handles all of this, out of the box. | |
| With a single pip install and one command to start Inference, you can set up a server that runs a fine-tuned model on any image or video stream. | |
| Inference supports running object detection, classification, instance segmentation, and even foundation models (like CLIP and SAM). You can train and deploy your own custom model or use one of the [50,000+ fine-tuned models shared by the community](https://universe.roboflow.com). |