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## `inference` without model? Is that possible?
Inference offers a way to expose models stub - which will not produce any meaningful predictions, but can be used for
several purposes:
* initial integration on your end with `inference` serving
* collecting dataset via `inference` Active Learning capabilities
## How stubs work?
Simply, create workspace and project at [Roboflow platform](https://app.roboflow.com). Once you are done - use the
client to send request to the API:
```python
import cv2
from inference_sdk import InferenceHTTPClient
CLIENT = InferenceHTTPClient(
api_url="http://localhost:9001", # if inference docker container is running locally
api_key="XXX"
)
image = cv2.imread(...)
CLIENT.infer(image, model_id="YOUR-PROJECT-NAME/0") # use version "0" to denote that you want stub model
```
As a result - you will receive the following response:
```json
{
"time": 0.0002442499971948564,
"is_stub": true,
"model_id": "asl-poly-instance-seg/0",
"task_type": "instance-segmentation"
}
```
You should not rely on response format, as it will change once you train and deploy a model, but utilising stubs
let you avoid integration cold start.
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