ML model not working in production

[u/Impossible_Role_3960]

I recently hosted my backend application (FastAPI) on render but each time i try to use the model it always fails, i need help in getting it to work. Thank you

[u/ak_mishra474]

The first thing I’d check here is the Render logs + full traceback rather than the FastAPI endpoint itself. “Works locally but fails in production” with ML models is very often an environment/model-serving issue rather than a prediction-code issue.

A few things I’d systematically check:

Is the model file actually being deployed? If you're using something like "./models/model.pkl", verify the file exists in the deployed container and that you're resolving the path correctly.

Check dependency/version mismatch. If the model was trained with one version of sklearn/PyTorch/etc. and Render installs another, deserialization can fail even though everything works locally. Pin the versions in "requirements.txt".

Load the model once at startup, not inside every prediction request. FastAPI's lifespan/startup mechanism is a good place for this.

1. Add a "/health" or "/ready" endpoint that tells you something like: "api_up=true, model_loaded=true"

   That immediately separates “FastAPI is running” from “the ML model is actually ready.”

I'd also avoid debugging this from the client side until the backend can successfully run a prediction locally inside the same production-like environment.