MLGatee takes a trained model file and returns a live HTTPS endpoint, with versions, rollback and monitoring built in.
A pickle can run code the moment it’s opened, so we never open yours on our servers. We read its bytes, pin the exact versions it needs, and refuse files that would run commands.
import os, requests url = "https://mlg-iris-7f3a9c21.onrender.com/predict" key = os.environ["MLGATEE_KEY"] res = requests.post(url, headers={"Authorization": f"Bearer {key}"}, json={"input_data": [[5.1, 3.5, 1.4, 0.2]]}) print(res.json()) # {'output': [0], 'model': 'iris'}
Make a new key on the model’s page. The old one keeps working until the new one is live, so nothing breaks in between. Keys are shown once and stored only as a hash.
Replacing or deleting one model never touches another. On the free plan an idle model naps after 15 quiet minutes and wakes on the next call.
Calls, errors and answer times, per hour and per version, for the last 24 hours or 7 days. Never your inputs, outputs or keys.
Same address. Same key. Every version.
Roll back in one click, or one line of Python, to any of your three newest versions. Same address, same key, so nothing that calls it has to change.
>>> mlgatee.deploy(net, name="digits",
... example_input=X[:5], task="classifier")
converting PyTorch → ONNX (opset 18)
checking against the original… same answers
uploading · inspecting · building
✓ live https://mlg-digits-…onrender.com (version 1)
Sign in once with an access token, then call mlgatee.deploy(model). Keras and PyTorch models are converted in your own Python and checked against the original first.
Upload a file or deploy from your notebook. MLGatee handles the packages, the server and the key.
Free plan: one model, no card needed