AI Model Deployment with Docker and FastAPI
Course provided by Model Institute of Engineering & Technology
5 modules
Explore the fundamentals of Artificial Intelligence & Machine Learning
5 Level NCrF
National Credit Framework
60 Hours
Flexible Learning
Beginner Level
No prior experience required
Micro Credit Course
02 Credit
Course Overview
This course offers a beginner-friendly, hands-on introduction to AI model deployment using Docker and FastAPI. Learners will gain practical experience packaging machine learning models into Docker containers and exposing them as RESTful APIs with FastAPI. The program focuses on containerization, API creation, and deployment workflows, enabling participants to deploy models quickly and efficiently in real-world environments. By the end of the course, students will be able to build scalable, production-ready ML services that can be integrated into applications and cloud platforms.
Key Learning Highlights
Build RESTful APIs for machine learning models using FastAPI
Package and deploy models inside Docker containers for portability
Understand containerization, dependency management, and versioning
Test, run, and expose ML services as microservices locally and on cloud
Deploy real-time prediction endpoints for production-ready applications
Tools & Platforms Used
Python
FastAPI
Docker
Uvicorn
Jupyter Notebook
Learning Outcome
By the end of this course, students will be able to:
Understand the fundamentals of API-based model deployment and containerization.
Build FastAPI applications for serving machine learning models.
Use Docker to containerize ML services for consistent deployment across environments.
Deploy real-time inference endpoints that can be tested and consumed via HTTP requests.
Create scalable, production-ready deployment pipelines for AI applications.
Master the course with just 5 Modules
This course takes learners from the fundamentals of Generative AI and prompt construction to advanced tuning techniques. Beginning with the principles behind AI-generated text and images, participants progress to hands-on experimentation with real-world use cases. The journey ends with a deep dive into emerging trends, ethical implications, and future opportunities in the rapidly evolving field of GenAI.
Foundations of API Development for ML Models
- Understanding REST APIs and HTTP protocols
- Overview of web frameworks; Introduction to FastAPI
- JSON structure, routing, and response models
- Local API testing with Swagger UI and Postman
Serving Machine Learning Models with FastAPI
- Loading and pre-processing ML models for inference
- Creating /predict and /health endpoints
- Input validation using Pydantic models
- Async APIs and middleware integration
- Logging and error handling
Containerization with Docker
- Introduction to Docker and its ecosystem
- Writing Dockerfiles for Python and ML environments
- Building, tagging, and pushing Docker images
- Running and inspecting containers
- Connecting containers to the host and external services
Advanced Deployment and Scalability
- Using Docker Compose for multi-service orchestration
- Exposing services via ports; volume mounts
- Environment variables and configuration management
- Brief intro to CI/CD integration with GitHub Actions or Jenkins
- Hosting on platforms like Heroku / Railway / Render (Optional)
Capstone Project – End-to-End AI Deployment
- Select or train an ML model (e.g., sentiment analysis, fraud detection)
- Build a FastAPI-based inference service
- Containerize the service with Docker
- Deploy the solution to a local/hosted platform
- Present a demo with documentation and GitHub repo
Roles
- MLOps Engineer
- AI Deployment Specialist
