MLGatee
Deploy trained models as key-protected HTTPS endpoints for notebook users.
MLGatee lets you upload a trained model or deploy it from a notebook to get a key-protected HTTPS endpoint. It inspects the model file, pins the needed package versions, and runs the model in its own service. Listed under Developer tools on UsefulShelf. Pricing: Free, Freemium and Subscription. Available on Web.
https://usefulshelf.co/apps/mlgatee
Overview
MLGatee lets you upload a trained model or deploy it from a notebook to get a key-protected HTTPS endpoint. It inspects the model file, pins the needed package versions, and runs the model in its own service.
The problem
It removes the need to send notebooks or run and manage model servers yourself.
How it works
Sign in, upload a model file or call the Python client from a notebook, let MLGatee inspect and deploy it, then call the generated HTTPS endpoint with a key.
Key features
- Upload model files or deploy from a notebook in one line of Python
- Inspect model bytes without loading the file
- Create a key-protected HTTPS endpoint for each model
- Roll back to recent versions and view calls, errors, and timings
What makes it different
It reads model files as bytes and never loads them on its servers; each model gets its own service and pinned package versions.
Use cases
Deploying a trained model from a notebook and calling it as an API. Serving classifier predictions with /predict and /predict_proba.
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Frequently asked questions
What is MLGatee?
MLGatee lets you upload a trained model or deploy it from a notebook to get a key-protected HTTPS endpoint. It inspects the model file, pins the needed package versions, and runs the model in its own service.
Who is MLGatee for?
People who train machine learning models, especially notebook users and developers.
How does MLGatee work?
Sign in, upload a model file or call the Python client from a notebook, let MLGatee inspect and deploy it, then call the generated HTTPS endpoint with a key.
What makes MLGatee different?
It reads model files as bytes and never loads them on its servers; each model gets its own service and pinned package versions.
When would someone use MLGatee?
Deploying a trained model from a notebook and calling it as an API. Serving classifier predictions with /predict and /predict_proba.
What are the key features of MLGatee?
Upload model files or deploy from a notebook in one line of Python. Inspect model bytes without loading the file. Create a key-protected HTTPS endpoint for each model. Roll back to recent versions and view calls, errors, and timings.
How is MLGatee priced?
Its listing shows a free, freemium and subscription pricing model. Check the official website for current plans and limits.
Where can I use MLGatee?
MLGatee is available on Web.
What technical traits does MLGatee have?
MLGatee is tagged as API.
Where is the official MLGatee website?
MLGatee is at mlgatee.com. UsefulShelf links to it directly from this page.
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