Introduction

As data science and machine learning workflows become more complex, containerization has emerged as a crucial technology for scalability, reproducibility, and deployment. Docker allows data scientists to package and deploy their models and applications consistently across different environments.

This course provides a hands-on, practical approach to using Docker for data science, covering essential topics such as containerization, Docker Compose, Kubernetes integration, and cloud deployment. Participants will learn how to containerize Jupyter notebooks, ML models, and data processing pipelines, making them scalable, reproducible, and production-ready.

Course Objectives

By the end of this course, participants will be able to:

• Understand containerization concepts and Docker’s role in data science workflows.
• Build, manage, and optimize Docker containers for machine learning and analytics.
• Work with Docker Compose for multi-container applications.
• Deploy ML models in containers using Flask, FastAPI, and TensorFlow Serving.
• Utilize Docker Hub and private registries for image management.
• Integrate Docker with Kubernetes and cloud platforms (AWS, GCP, Azure).
• Implement best practices for security, scalability, and DevOps in data science.

Who Should Attend?

This course is ideal for:

• Data scientists looking to package and deploy models in reproducible environments.
• ML engineers who need to scale and manage machine learning applications.
• Software developers working with data-intensive applications.
• DevOps engineers supporting ML and analytics teams.
• Cloud architects designing AI/ML workflows in containerized environments.

Day-by-Day Course Breakdown

Day 1: Introduction to Docker & Containerization

Understanding Containerization for Data Science

• What is containerization? Benefits for data science and ML.
• Introduction to Docker, Containers, and Images.
• Setting up Docker on Windows, macOS, and Linux.

Building and Running Docker Containers

• Writing Dockerfiles for data science applications.
• Running containers with docker run, docker ps, and docker exec.
• Hands-on lab: Containerizing a Jupyter Notebook environment.

Day 2: Managing Data Science Containers

Optimizing Docker Images

• Reducing image size with best practices for efficient builds.
• Multi-stage builds for ML and analytics applications.
• Hands-on lab: Creating a lightweight ML image with TensorFlow/PyTorch.

Working with Docker Volumes & Networks

• Managing persistent data storage in Docker.
• Connecting containers using Docker networks.
• Hands-on lab: Running an ML pipeline with persistent storage.

Day 3: Multi-Container Applications & Model Deployment

Orchestrating Multi-Container Applications with Docker Compose

• Introduction to Docker Compose for multi-service applications.
• Defining ML workflows in docker-compose.yml.
• Hands-on lab: Containerizing a FastAPI-based ML model with a database.

Deploying Machine Learning Models in Docker

• Serving models with Flask, FastAPI, and TensorFlow Serving.
• Running ML inference in containers.
• Hands-on lab: Deploying a trained ML model as an API in Docker.

Day 4: Cloud Deployment & Kubernetes Integration

Pushing Docker Images to Registries

• Working with Docker Hub, AWS ECR, and Google Artifact Registry.
• Automating builds with GitHub Actions & CI/CD pipelines.
• Hands-on lab: Pushing a containerized ML model to Docker Hub.

Introduction to Kubernetes for Data Science

• Scaling ML applications with Kubernetes (K8s).
• Deploying containerized models to Google Kubernetes Engine (GKE) & AWS EKS.
• Hands-on lab: Deploying a containerized Jupyter Notebook on Kubernetes.

Day 5: MLOps & Advanced Docker Use Cases

Docker for MLOps & Workflow Automation

• Automating model retraining and deployment with CI/CD pipelines.
• Using Airflow & Prefect for workflow automation in containers.
• Hands-on lab: Creating an automated ML pipeline in Docker.

Capstone Project: End-to-End Containerized Data Science Application

• Participants will design, build, and deploy a complete containerized ML application.
• Model training, API deployment, and cloud integration.
• Final presentations and peer review.

Conclusion & Certification

At the end of the training, participants will receive a Certificate of Completion, demonstrating their expertise in Containerization with Docker for Data Science.

This course combines theory, hands-on labs, and real-world case studies to equip learners with modern containerization skills for AI & data science workflows.