🧠 ML with Docker – Model Serving & Monitoring Dashboard

A fully containerized machine learning app that allows users to:

• Upload data for predictions via a Streamlit dashboard.
• Serve a trained ML model using FastAPI.
• Deploy using Docker or Docker Compose.

This project demonstrates the end-to-end DevOps flow of building, containerizing, and deploying a Python-based ML application.

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📌 Features

• 🔮 Prediction Service: FastAPI backend serving an Iris classifier model.
• 📊 User Dashboard: Streamlit frontend for uploading CSVs and viewing results.
• 🐳 Dockerized: Easily portable and deployable using Docker/Docker Compose.
• 🔁 Stateless API: Send JSON data and get back predictions.

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🧰 Tech Stack

• Python 3.10
• scikit-learn
• FastAPI
• Streamlit
• Docker

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🚀 Getting Started

🔧 Prerequisites

• Python 3.10+
• Docker
• (Optional) Docker Compose

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🛠️ Setup Instructions

🔁 1. Clone the Repository

git clone https://github.com/sanjai14/ml_with_docker.git
cd ml_with_docker