Deployment of Machine Learning Models Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
A hands-on course for deploying machine learning models using practical tools like Flask, FastAPI, Streamlit, and Docker. This course covers end-to-end deployment workflows, from creating REST APIs to containerization and cloud deployment. With approximately 5 hours of total content, learners will gain practical experience in production-ready ML deployment techniques through step-by-step projects and real-world use cases.
Module 1: Introduction to Model Deployment
Estimated time: 0.5 hours
- Why deployment is essential in ML lifecycle
- Overview of deployment strategies: batch, online, and real-time
Module 2: Creating REST APIs with Flask
Estimated time: 0.75 hours
- Converting ML models into RESTful APIs
- Building backend services using Flask
Module 3: Deploying with FastAPI
Estimated time: 1 hour
- Advantages of FastAPI over Flask for ML
- Creating scalable and high-performance ML APIs
Module 4: Building ML Web Apps with Streamlit
Estimated time: 1 hour
- Interactive frontends for ML models using Streamlit
- Deploying Streamlit apps locally and on the cloud
Module 5: Model Deployment with Docker
Estimated time: 1 hour
- Dockerizing ML projects for consistent environments
- Running and managing containers for deployment
Module 6: Deployment on Cloud Platforms
Estimated time: 0.75 hours
- Overview of deployment on Heroku, AWS, and other platforms
- Pushing models to production environments
Module 7: End-to-End Project Deployment
Estimated time: 1.25 hours
- Full ML app deployment from training to production
- Code structure, version control, and CI/CD tips
Prerequisites
- Basic understanding of Python programming
- Familiarity with machine learning model development
- Basic knowledge of web services and APIs
What You'll Be Able to Do After
- Deploy machine learning models using Flask and FastAPI
- Build interactive web applications for ML models with Streamlit
- Containerize ML applications using Docker
- Deploy models on cloud platforms like Heroku and AWS
- Implement end-to-end deployment workflows from model training to production