Sharing model parameters with monitoring
You would like to add a health check endpoint that provides model parameters to your penguin classification API.
The required packages (FastAPI and joblib) have been already imported.
Deze oefening maakt deel uit van de cursus
Deploying AI into Production with FastAPI
Oefeninstructies
- Add a GET endpoint at the typical location for health checks.
- Capture the model parameters from the sklearn model using the
get_paramsmethod. - Include the model parameters in the response as the value to key
params.
Praktische interactieve oefening
Probeer deze oefening eens door deze voorbeeldcode in te vullen.
model = joblib.load(
'penguin_classifier.pkl'
)
app = FastAPI()
# Create health check endpoint
@app.get("____")
async def get_health():
# Capture the model params
params = ____.get_params()
return {"status": "OK",
# Include model params in response
"params": ____}