Deploying Production-Ready Machine Learning APIs with FastAPI and Docker

Course Overview

Deploying machine learning models into production is one of the most critical skills for a modern data scientist or ML engineer. This course takes you on a comprehensive journey from understanding the fundamentals of APIs to building, validating, containerizing, and deploying high-performance machine learning microservices using FastAPI, Pydantic, Docker, and AWS.

What You'll Learn

• API Fundamentals: Understand how client-server communication works and why APIs are essential for ML.
• FastAPI Core: Master routing, path/query parameters, HTTP methods (GET, POST, PUT, DELETE), and status codes.
• Pydantic Validation: Implement robust data validation, custom field/model validators, and structured serialization.
• Model Serving: Learn how to serialize ML models and serve predictions via API endpoints.
• Docker Containerization: Package your FastAPI application with Docker to ensure consistency across environments.
• Cloud Deployment: Deploy your containerized API on AWS EC2 instances with real-world security configurations.

Prerequisites

• Basic Python programming knowledge.
• Familiarity with foundational machine learning concepts (training and saving models).
• Basic understanding of command-line interfaces.

Tools & Technologies

• Language: Python
• Web Framework: FastAPI, Uvicorn
• Data Validation: Pydantic (v2)
• Containerization: Docker, Docker Hub
• Cloud Platform: AWS (EC2)
• ML Libraries: Scikit-Learn, Pandas, NumPy