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Fastapi Ml Endpoint

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jeremylongshore·vlatest·backend·MIT

Deploy machine learning models as FastAPI endpoints quickly using this reusable agent from jeremylongshore.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add jeremylongshore/tons-of-skills-marketplace --skill fastapi-ml-endpoint -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

jeremylongshore

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Install target

Live repository

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About this skill

What it does

This skill allows you to deploy machine learning models as FastAPI endpoints. It handles the creation of a basic API server, model loading, and prediction serving, enabling seamless integration of ML models into applications. The endpoint accepts input data, processes it using the loaded model, and returns predictions in a structured format.

When to use it

  • Rapid Prototyping: Quickly expose a machine learning model for testing or demonstration purposes without extensive infrastructure setup.
  • Internal APIs: Create simple internal APIs for other services or applications to consume your ML models.
  • Model Serving: Provide a basic serving solution for deployed models, suitable for low-traffic scenarios or initial deployments.
  • Integration with Frontend: Allow frontend applications (e.g., web apps) to easily send data and receive predictions from an ML model.

Key capabilities

  • FastAPI endpoint creation
  • Model loading
  • Prediction serving
  • Input data handling
  • Structured output formatting

Example prompts

  • "Create a FastAPI endpoint for my image classification model, my_model.pth."
  • "Deploy my sentiment analysis model and expose it as an API with the path /predict."
  • "Generate an endpoint that accepts text input and returns a sentiment score from my pre-trained model."

Tips & gotchas

  • Ensure your machine learning model is saved in a compatible format (e.g., .pth, .h5).
  • The skill provides a basic API; more complex deployments might require additional configuration or infrastructure.