Containerization for Python: Docker and Kubernetes

Modern software development requires more than just writing code; it requires knowing how that code runs in any environment. Containerization has become the standard for ensuring Python applications and data science models work reliably from development to production without the headache of dependency conflicts. This course guides you from the fundamental concepts of virtualization to managing complex containerized systems. You will gain the skills to isolate environments, simplify dependency management, and orchestrate Python-based services at scale, whether you are building web APIs or deploying machine learning models. What you'll learn: - Understand the core concepts of containerization and how it differs from traditional virtualization. - Build efficient Docker images for Python applications using modern packaging tools and lightweight base images. - Manage multi-container environments for data science workflows and microservices. - Deploy and scale Python services using Kubernetes orchestration and cluster management. - Configure persistent storage and networking for containerized Python databases and applications. - Apply modern best practices for container security and image optimization. The curriculum begins with foundational terminology and the architecture of container engines before moving into practical image creation and orchestration strategies. You will read through detailed explanations of configuration files and deployment manifests used in professional environments to ensure your code is production-ready. This course is designed for beginner Python developers, data scientists, and machine learning engineers who have no prior experience with containers. No previous knowledge of DevOps or cloud infrastructure is required. Start building portable and scalable Python environments today.