A cloud-native iris ML inference service built with FastAPI, containerized using Docker, locally tested on Kubernetes (Minikube), and designed for scalable AWS deployment.

A lightweight Iris classifier that trains a scikit-learn model and serves it via FastAPI inside a Docker container—designed for easy testing, reproducible deployments, and seamless pushing to Azure container registries and services.

ML microservice with FastAPI + Docker + CI

FastAPI service for iris flower species inference using scikit-learn's RandomForestClassifier. Available as a Docker image.

A machine learning project that classifies iris flowers using a Random Forest model, deployed as a FastAPI web service.

A production-ready FastAPI service that wraps a Scikit-Learn RandomForest classifier for predicting Iris species. Includes Docker support for easy deployment.

A production-ready FastAPI service that wraps a Scikit-Learn RandomForest classifier for predicting Iris species. Includes Docker support for easy deployment.

Training a model to predict iris flower species using the Iris dataset. Developing a REST service using the FastAPI library that will return the predicted species based on the passed flower parameters. The developed service can be run as a Docker container.

A minimal end-to-end example of deploying a FastAPI + pandas app on local Kubernetes (Minikube). ## Features - FastAPI web server - Loads Iris dataset and returns statistics - Dockerized - Deployed with Kubernetes Deployment (2 replicas) - Exposed via Service + port-forward

TP2 : Docker et Docker Compose pour MLOps Objectifs — Containeriser un service IA (API FastAPI + Frontend React). — Construire et tester chaque image individuellement avant l’orchestration. — Utiliser Docker Compose pour orchestrer plusieurs conteneurs. Projet de référence Dépôt à forker : https://gitlab.com/mlops_tps/iris-ai-service

A complete machine learning web service that classifies Iris flowers using FastAPI and scikit-learn. Features a trained Logistic Regression model (96.67% accuracy) with robust input validation, comprehensive error handling, and an interactive web interface for real-time predictions.

This repository contains a simple web API for classifying Iris flower species. It uses a logistic regression model, with the API built on FastAPI and served with Uvicorn for high performance. It showcases the steps for deploying a machine learning model as a web service