Containerizing and Deploying ML Models with Docker

Course Description

This short course provides an end-to-end guide on how to take a simple Machine Learning (ML) model and seamlessly package and deploy it using Docker. You’ll learn the fundamentals of Docker and how to create and run containers, then walk through building a minimal ML model (in Python), writing a Dockerfile to containerize it, and ultimately deploying the container to a production environment. By the end of this course, you’ll be equipped with practical Docker knowledge and best practices to ship your ML applications more efficiently.

Target Audience

• Data Scientists who want to learn DevOps basics and streamline their ML deployment process.
• Software Engineers interested in adding ML containerization to their skill set.
• ML/AI Enthusiasts looking to level up their deployment skills.
• Students or developers who are relatively new to Docker and want hands-on experience deploying an ML model.

Key Learning Outcomes

1. Docker Fundamentals
   • Understand what containers are, why they’re used, and how Docker streamlines software delivery.
2. ML Model Preparation
   • Build and export a basic ML model using common Python libraries (e.g., scikit-learn).
3. Dockerfile Creation & Image Building
   • Write a Dockerfile to package your Python application and dependencies into an image.
4. Running and Testing Containers
   • Launch Docker containers and validate your ML model with real data.
5. Deployment Best Practices
   • Explore multi-stage builds, Docker Compose, security, and CI/CD workflows for production-grade container deployment.
6. Hands-On Experience
   • Gain practical skills by building, running, and pushing a containerized ML model to a registry.