Deploying Production-Ready Machine Learning APIs with FastAPI and Docker
Master the deployment of robust machine learning models as high-performance APIs. Learn FastAPI, Pydantic data validation, Docker containerization, and AWS EC2 deployment to build production-ready ML microservices from scratch.
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
Course Content
Introduction to APIs and FastAPI Basics
What is an API? | Introduction to APIs
FastAPI Philosophy & Environment Setup
HTTP Methods in FastAPI
Parameters, Request Bodies, and Pydantic Validation
Path and Query Parameters in FastAPI
Data Validation with Pydantic
Request Bodies and POST Requests
Updating and Deleting Data with PUT and DELETE
Serving Machine Learning Models via API
Serving ML Models with FastAPI
Improving the FastAPI API
Containerization and AWS Deployment
Docker Fundamentals for Machine Learning
Dockerizing a FastAPI Machine Learning API
Deploying Containerized APIs to AWS EC2
This course includes
- 8h 41m on-demand video
- 12 lessons across 4 chapters
- 4 interactive quizzes
- Access on mobile and desktop
- Source playlist on YouTube
About the Channel
Hello, I'm Nitish Singh, the founder of CampusX, your online gateway into the world of data science! At CampusX, we believe that quality education is a universal right, not a privilege. We aim to provide TOP TIER data education to everyone with a passion to learn. On our YouTube channel, we provide high quality free content covering everything related to data. Apart from this, we also provide paid mentorship programs for students where enrolled students get the benefit of personalized guidance through live and recorded video lectures, daily skill-building activities, hands-on projects, assignments, and interactions with industry experts. Leveraging my 10 years of teaching experience in the data industry, primarily in Machine/Deep Learning, I bring real-life industry experiences directly to your learning journey. Explore our video courses at https://learnwith.campusx.in/ for in-depth technology lessons. For all business inquiries, please reach out to support@campusx.in
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