DOCKER FUNDAMENTALS & INFRASTRUCTURE

Containerization, Orchestration & Production Deployment Patterns

Mastering Docker from conceptual foundations to production-grade multi-container orchestration. This portfolio demonstrates Docker's role in modern CI/CD pipelines, MLOps workflows, and scalable infrastructure design across single-container applications to complex microservices architectures.

Docker Conceptual Foundations

Understanding Docker architecture, components, and how it fits into modern development workflows, CI/CD pipelines, and MLOps ecosystems.

Docker Architecture

Docker follows a client-server architecture with key components that work together to build, ship, and run containers.

Docker Client: CLI interface for users
Docker Daemon: Background service managing containers
Docker Host: Physical/virtual machine running containers
Docker Registry: Repository for Docker images

Images vs Containers

Understanding the fundamental building blocks of Docker: immutable images and running containers.

Dockerfile: Blueprint for building images
Image: Immutable template with application code
Container: Running instance of an image
Layers: Efficient storage through layer caching

Networks & Volumes

Isolation and persistence mechanisms that enable container communication and data management.

Bridge Network: Default network for container communication
Host Network: Container shares host's network namespace
Volumes: Persistent data storage outside containers
Bind Mounts: Direct host filesystem access

Registries & Distribution

Image storage, distribution, and management across development, testing, and production environments.

Docker Hub: Public registry for Docker images
Private Registries: AWS ECR, Azure Container Registry
Image Tagging: Versioning and environment tagging
Image Security: Scanning and vulnerability management

Docker in CI/CD

How Docker integrates with modern CI/CD pipelines for consistent build, test, and deployment environments.

Build Automation: Consistent build environments
Testing Isolation: Isolated test environments
Artifact Management: Docker images as deployment artifacts
Environment Parity: Dev/Test/Prod consistency

Docker in MLOps

Containerization of ML models, dependencies, and inference services for reproducible machine learning workflows.

Model Packaging: Containerized ML models
Dependency Management: Reproducible environments
Scalable Inference: Containerized inference services
Experiment Tracking: Versioned model containers

Docker Architecture & Workflow

Docker Client

CLI, Docker Desktop, API

→

Docker Daemon

Manages containers, images, networks

Dockerfile

Build instructions

→

Docker Image

Immutable template

→

Docker Container

Running instance

Docker Registry

Docker Hub, ECR, ACR

↔

Docker Host

Server/VM running containers

Docker CLI Fundamentals

Command	Description	Example
docker version	Display Docker version information	`docker version`
docker images	List locally available images	`docker images`
docker build	Build an image from a Dockerfile	`docker build -t myapp:v1 .`
docker run	Create and start a container	`docker run -p 8080:80 myapp:v1`
docker ps	List running containers	`docker ps -a`
docker stop/start	Stop or start a container	`docker stop container_id`
docker logs	Fetch container logs	`docker logs container_id`
docker exec	Execute command in running container	`docker exec -it container_id bash`
docker push/pull	Push/pull images to/from registry	`docker push myrepo/myapp:v1`
docker network	Manage Docker networks	`docker network create mynet`
docker compose	Multi-container orchestration	`docker compose up -d`

Docker Projects

Three practical Docker implementations demonstrating containerization from basic applications to production-ready systems.

Basic

Single-Container Node.js App

Beginner

Dockerizing a Node.js web application with proper Dockerfile practices, port mapping, and Docker Hub deployment.

Node.js Express Dockerfile Docker Hub Port Mapping

Dockerfile with multi-stage builds

Port mapping and network configuration

Docker Hub image push and versioning

CLI execution with logs and monitoring

MLOps

ML Inference Service

Intermediate

Containerized machine learning model serving with REST API, Flask backend, and production-ready inference pipeline.

Flask ML Model REST API Gunicorn Scikit-learn

ML model packaging and dependencies

REST API for model predictions

High-performance inference with Gunicorn

Terminal & Postman testing

Advanced

Multi-Container Orchestration

Advanced

Production-grade multi-service application with Docker Compose, service discovery, and inter-container communication.

Docker Compose Microservices Networking Service Discovery Volumes

Multi-service architecture with Docker Compose

Service networking and discovery

Environment-based configuration

End-to-end invocation and testing

Docker Workflow Automation

docker-workflow.sh

$ docker build -t myapp:v1 .

Building Docker image...

✓ Image built successfully: myapp:v1

$ docker run -d -p 8080:80 --name webapp myapp:v1

✓ Container started: webapp (id: a1b2c3d4)

$ docker ps

CONTAINER ID IMAGE STATUS PORTS NAMES

a1b2c3d4 myapp:v1 Up 2s 0.0.0.0:8080->80/tcp webapp

✓ Application running at http://localhost:8080