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Introduction to Machine Learning Deployment

Chapter 1: Getting Started with Model Deployment

What is Machine Learning Deployment?

Why Deploy Machine Learning Models?

The Machine Learning Workflow Overview

Types of Deployment Strategies (Introduction)

Challenges in Model Deployment

Chapter 2: Preparing Your Model for Deployment

Saving Trained Models

Introduction to Model Serialization

Using Pickle for Model Persistence

Using Joblib for Model Persistence

Handling Model Dependencies

Saving Preprocessing Steps

Hands-on Practical: Saving and Loading a Simple Model

Chapter 3: Creating a Prediction Service with Flask

What is an API?

Introduction to Web Frameworks

Setting Up Flask

Creating a Basic Flask Application

Loading Your Saved Model in Flask

Defining a Prediction Endpoint

Handling Input Data (JSON)

Returning Predictions

Testing Your API Locally

Hands-on Practical: Building a Simple Flask Prediction API

Chapter 4: Introduction to Containerization with Docker

What is Containerization?

Introduction to Docker

Docker Concepts: Images and Containers

Installing Docker

Writing a Simple Dockerfile

Building a Docker Image for the Flask App

Running the Application in a Docker Container

Hands-on Practical: Containerizing the Prediction Service

Introduction to Machine Learning Deployment

Prerequisites: Python and ML Basics

Level: Beginner

What You'll Learn

Model Deployment Concepts
Understand why model deployment is necessary and its role in the machine learning lifecycle.
Model Preparation
Learn how to save and load trained machine learning models using common serialization techniques.
API Development Basics
Understand the concept of an API and how it's used for model serving.
Simple Web Service Creation
Build a basic web service using Flask to serve predictions from a trained model.
Containerization Introduction
Grasp the fundamentals of containerization with Docker for packaging applications.

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