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Simplifying ML Model Deployment and Tracking with Docker and MLflow

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Simplifying ML Model Deployment and Tracking with Docker and MLflow

Introduction This project aims to simplify the deployment, tracking, and management of machine learning models using Flask, Docker, and MLflow. It provides an easy-to-use Flask API for model serving and utilizes MLflow for model tracking and performance metrics.

Table of Contents

  • Introduction
  • Features
  • Prerequisites
  • Installation
  • Usage
  • Contributing
  • Features
  • Flask API for model serving
  • MLflow for model tracking and performance metrics
  • Dockerized setup for hassle-free deployment

Prerequisites

  • Python 3.10
  • Docker
  • Docker Compose

Installation

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Simplifying ML Model Deployment and Tracking with Docker and MLflow

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