Developer tools
MLGatee
Deploy trained models as endpoints without managing servers or containers.
- Category
- Developer tools
- Stage
- Growth
- For
- Developers and data scientists who want to deploy trained machine-learning models as APIs without managing infrastructure.
About
Upload a trained model, or deploy it from your notebook in one line of Python. In about four minutes you get a key-protected HTTPS address that anyone you choose can call. No Docker, no servers, no pip.
Features
- Problem
- Deploying a model from a notebook can involve a separate infrastructure workflow.
- How it works
- The Python client deploys a model with mlgatee.deploy(), while the web app also accepts model files.
- Result
- A live endpoint is typically available in about 1.5 to 4 minutes.
- Why
- Lets developers use a notebook workflow or upload a file directly.
- Problem
- Loading untrusted pickle files can run embedded code, and mismatched packages can prevent deployment.
- How it works
- MLGatee inspects file bytes without loading the model, refuses files that would run commands, and pins checked package versions.
- Result
- The model is built with the versions detected from its file.
- Why
- Avoids executing the uploaded model during inspection and identifies its package requirements.
- Problem
- A deployed model needs a way to limit who can call it.
- How it works
- Each endpoint uses a key sent with HTTP requests; keys are shown once and stored as hashes.
- Result
- Authorized clients can call the model through its HTTPS endpoint.
- Why
- Provides access control while limiting exposure of the full key.
- Problem
- A faulty update can disrupt clients using a model endpoint.
- How it works
- MLGatee keeps the three newest versions and supports rollback to an earlier version at the same address and key.
- Result
- Clients can continue using the same endpoint after a rollback.
- Why
- Allows a previous model version to be restored without changing client configuration.
- Problem
- Operators need deployment and usage visibility without collecting model request contents.
- How it works
- Monitoring reports call counts, errors, and response times; inputs and outputs are not recorded.
- Result
- Users can review endpoint activity by time period and version.
- Why
- Provides operational signals while excluding request and response data from monitoring.
Product questions
What does MLGatee do?
MLGatee turns trained machine-learning models into key-protected HTTPS API endpoints. Deploy through a web upload or a one-line Python client, without managing servers or containers.
Who is MLGatee for?
Developers and data scientists who want to deploy trained machine-learning models as APIs without managing infrastructure.
Screenshots
