Can I use these templates with any machine learning framework?

Yes, the templates are designed to be framework-agnostic, supporting scikit-learn, PyTorch, TensorFlow, and ONNX models via standardized loading patterns.

How does this skill handle large-scale inference?

The FastAPI templates include built-in batch prediction support and async request handling to manage multiple inference requests efficiently.

Does it provide UI components for model results?

Yes, it includes React components for Next.js that feature visual feedback, such as confidence bars and probability distributions using shadcn/ui.

What frameworks does this skill support for ML APIs?

It primarily focuses on FastAPI for high-performance Python-based inference APIs and provides TypeScript templates for Next.js frontend integration.

ML Integration Helpers

Name: ML Integration Helpers
Author: vanman2024

byvanman2024

データサイエンスとML

Deploys machine learning models into full-stack applications using production-ready FastAPI endpoints, Next.js UI components, and Supabase schemas.

概要

This skill streamlines the transition from a trained model to a production-grade application by providing standardized integration templates for the modern web stack. It automates the creation of robust FastAPI inference routers with Pydantic validation, scaffolds responsive Next.js prediction interfaces using shadcn/ui, and implements scalable Supabase database schemas for model versioning and inference logging. Whether you are deploying classification, regression, or image-based models, this skill ensures best practices in error handling, batch processing, and result visualization are baked into your application from the start.

主な機能

FastAPI router generation for various model types including classification and embeddings
Type-safe Pydantic request and response models for strict inference contracts
Pre-built Next.js React components for prediction forms and result visualization
Standardized Supabase SQL schemas for tracking model metadata and training runs
Efficient singleton model loading patterns with built-in caching and batch support
0 GitHub stars

ユースケース

Deploying a scikit-learn or PyTorch model as a high-performance REST API
Establishing a backend system for logging ML model versions and performance metrics
Building a web-based dashboard for users to interact with real-time AI predictions

概要

主な機能

FastAPI router generation for various model types including classification and embeddings
Type-safe Pydantic request and response models for strict inference contracts
Pre-built Next.js React components for prediction forms and result visualization
Standardized Supabase SQL schemas for tracking model metadata and training runs
Efficient singleton model loading patterns with built-in caching and batch support
0 GitHub stars

ユースケース

Deploying a scikit-learn or PyTorch model as a high-performance REST API
Establishing a backend system for logging ML model versions and performance metrics
Building a web-based dashboard for users to interact with real-time AI predictions