Model Deployment Guide

Welcome to the Model Deployment Guide! This guide provides practical, hands-on resources to help you deploy machine learning models effectively. Starting with an introduction to deployment, each chapter explores specific tools and methods for making your models accessible in real-world applications.

Chapters

• Introduction to ML Deployment - Understand the basics and importance of deploying machine learning models. Available
• Prototyping and Sharing ML Models - Create interactive web apps using tools like Streamlit and Gradio. Available
• API Deployment Using Flask - Learn how to build and deploy APIs with Flask. Available
• API Deployment Using FastAPI - Explore FastAPI for creating fast, production-ready APIs. Available
• Containerization with Docker - An optional guide to packaging ML applications with Docker for easy deployment. Partially Available
• Serverless Deployment - Deploy ML models without managing servers using platforms like AWS Lambda. Coming Soon
• Model Deployment on Cloud Services - Learn to deploy models on platforms like AWS, GCP, or Azure. Coming Soon
• Edge and IoT Deployment - Bring ML models to edge devices like Raspberry Pi or mobile phones. Coming Soon
• Model Monitoring and Maintenance - Keep your deployed models reliable and up-to-date. Coming Soon