Udemy – Deploy ML Model in Production with FastAPI and Docker 2025-3

Deploy ML Models in Production with FastAPI and Docker. This course teaches you how to take machine learning models from the experimental stage to production applications. Using FastAPI and Docker, you’ll learn the complete process of deploying ML models, from building powerful APIs to containerizing and deploying them to cloud platforms like Heroku and Microsoft Azure. The course includes hands-on projects like score prediction, wine quality classification, and lily species identification that help you learn industry best practices in API development, error management, and performance optimization. You’ll also gain the skills you need for production environments by building CI/CD pipelines and deploying scalable solutions. This course is designed for data scientists and developers who want to implement their models in real systems. By the end of the course, you will have the ability to build and deploy ML systems from start to finish and can demonstrate these skills to employers as a competitive advantage.

What you will learn

• Deploy machine learning models in production using FastAPI and Docker.
• Create APIs for ML models with FastAPI and optimized endpoints.
• Containerize ML applications with Docker for scalable deployments.
• Setting up CI/CD pipelines for automated deployment and testing.
• Train, evaluate, and store ML models, focusing on real-world datasets.
• Deploy ML models on cloud platforms such as Heroku and Microsoft Azure.
• Build and integrate a simple frontend for ML model APIs.
• Implement logging, error handling, and request management in APIs.

This course is suitable for people who:

• Data scientists are eager to learn model deployment.
• Machine learning engineers aiming to increase deployment skills.
• Software developers interested in integrating ML into applications.
• Tech enthusiasts curious about Docker, FastAPI, and cloud deployments.
• Professionals transitioning into MLops or AI engineering roles.
• Students with basic Python knowledge who are looking to build ML projects from start to finish.

Course details: Deploy ML Model in Production with FastAPI and Docker

• Publisher:  Udemy
• Instructor:  Meta Brains , Skool of AI
• Training level: Beginner to advanced
• Training duration: 3 hours and 57 minutes
• Number of lessons: 57

Course syllabus in 2025/5

Prerequisites for the Deploy ML Model in Production with FastAPI and Docker course

• Basic knowledge of Python programming.
• Familiarity with machine learning concepts and workflows.
• A computer with internet access for software setup.
• Willingness to learn and experiment with new tools like Docker and FastAPI.

Course images

Sample course video

Download link

Download Part 1 – 1 GB

Download Part 2 – 563 MB

File(s) password: www.downloadly.ir

File size

1.5 GB