Katana project is a FastAPI template for ASAP 🚀 ML API deployment

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The repository host the API for the ML model via FastAPI, Flask and contains android app development files using KivyMD.

Building a simple FastAPI application for model inference.

A complete end-to-end machine learning portal that covers processes starting from model training to the model predicting results using FastAPI.

Tensorflow ML API Template for Text and Image Input using FastAPI 🚀

A FastAPI based API for the insurance premium prediction ML project

Scaffolding for serving ml model APIs using FastAPI

A fully featured banking API built with FastAPI,Docker,Celery,Redis,RabbitMQ with an AI/ML transaction analysis and fraud detection system

This repository about how to deploy machine learning model end serving with FastAPI and using MLFlow-MINIO

Learning and buiding API using Fast API

Template repository of a machine-learning Python project powered by FastAPI and PyTorch

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FastAPI backend for Chinese dialect geolinguistics research — phonological queries, Praat acoustic analysis, ML-based spatial clustering, and village dialect mapping APIs.

This repo focuses on how to rapidly deploy your machine learning models with FastAPI, Redis and Docker.

Ames Housing Price Predictor is an ML project focused on predicting housing prices in Ames, Iowa. Development spans all stages of the machine learning model lifecycle, from data exploration and cleansing, through feature engineering and model selection, to deploying a working API using FastAPI, Docker, and GCP.

fastAPI used microservice creation and ml model

ML repository delivering both scheduling via cron jobs and API endpoints for running ETL, training, prediction, evaluation, etc, of various end-to-end models. Developed with FastAPI, TensorFlow, PyTorch, AWS, MongoDB.

How to Deploy Machine Learning Models with FastAPI and AWS Lightsail

FastAPI API Development Booth: A comprehensive repository showcasing best practices and examples for building robust and efficient API using FastAPI in Python.

ML Engineering best practices | ML model deployment as a FastAPI service, containerized with Docker for scalability and reproducibility.

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Containerization of an ML model converted into an API using FastAPI.

ML Engineering best practices | Deploying an ML API with FastAPI, PostgreSQL, and Docker for scalable model storage and inference.

Simple ML API built on FastApi for classifying breed of dog based on uploaded image with provided token authorization. PyTorch CNN model trained on Stanford Dogs dataset

Customer Segmentation using KMeans clustering with a full ML pipeline including data preprocessing, model training, API serving via FastAPI, visualization with Streamlit dashboard, logging, error handling, and Docker deployment.

Deploy ML model with Fast API on localhost

Backend API built with FastAPI that performs claim detection, ML-based probability scoring, and LLM explanation for news verification.

Monorepo untuk Proyek Capstone A25-CS050 (Predictive Maintenance Copilot). Berisi layanan Backend (Express), Frontend (React), dan ML-API (FastAPI).

End-to-end ML project for phishing website detection with FastAPI, scikit-learn, and MongoDB. Includes automated training pipeline and REST API deployment.