
In this hands-on session, you'll set up a powerful AI assistant right inside Docker containers to supercharge your learning journey throughout this course. We'll walk through installing and running Ollama with the DeepSeek model locally using Docker, giving you instant access to an intelligent coding companion that understands Docker, containerization, and frontend development.
What You'll Learn:
Deploy Ollama as a Docker container for local AI model hosting
Pull and run the DeepSeek model for coding assistance
Test your setup with Docker and frontend development queries
Why This Matters: Before diving into Docker concepts, you'll have your own AI tutor running locally. This setup allows you to ask questions, get code explanations, debug container issues, and receive personalized guidance as you progress through the course - all without relying on external APIs or internet connectivity.
Prerequisites:
Docker Desktop installed on your machine
Basic command line familiarity
By the end of this lecture, you'll have a fully functional local AI environment that will serve as your learning companion throughout the entire Docker course, making complex concepts more accessible and providing instant help when you encounter challenges.
Command Referenced in the lecture
docker run -it --rm -v ollama:/root/.ollama -p 11434:11434 ollama/ollama
Automate development environment setup with Docker files and Docker Compose, enabling seamless configuration for frontend, backend, and database services while boosting collaboration and efficiency.
Explore common deployment approaches such as ftp and SftP secured FTP, git-based deployments, CI, CD pipelines, cloud-based deployments, and containerization, with a focus on their pros and cons.
Use ftp or sftp for simple static websites by manually uploading files with a client like FileZilla, reflecting changes instantly, but it lacks automation and version control.
Git based deployment uses git hooks and a deployment script to automate deployments to production servers, aligning with git workflows but not suited for complex dependencies.
Explore how ci/cd pipelines automate deployment through continuous integration, continuous deployment, and testing, deploying to production when tests pass, and their setup complexity for agile teams with frequent deployments.
Dive into the world of Docker by launching a hello-world Docker container.
Build your own Docker image from scratch for a Node.js API.
Learn the client-server architecture of Docker.
Learn how docker images are structured.
List local Docker images with Docker image ls to view repository, tag, image id, creation time, and size, and identify dangling images and service-driven images from Docker Compose.
Learn how to pull Docker images from Docker Hub, verify publisher details, use the Docker pull command, and understand local image caching.
Integrate Docker into a Next.js app by creating a Dockerfile from a Node.js image, installing dependencies, building, and running on port 3000 with a .dockerignore for node_modules.
Build docker compose from scratch to run a Node API and a Next.js app together, with ports exposed and get server side props fetching data from the API.
Publish your docker images to Docker Hub by logging in, tagging images with your handle, and pushing to a public or private repository for cloud deployment.
In this bonus lesson, you will be introduced to the Astro framework. You will learn how to create a new Astro app from scratch and build a Dockerfile for Server Side Rendering Astro app on Node.js.
You will learn the necessary skills to use Docker in your development work. This course takes a hands-on approach. Learn how to run and manage Docker containers, build docker images, debug Docker issues and use Docker Compose to run multiple containers together.
Have you encountered situations when an application worked on a developer's local machine but failed on other devices? Have you spent time diagnosing issues caused by misconfiguration? Are you frustrated with being unable to run code deployed in production on your local machine to diagnose production issues? Docker is here to help you in fixing these issues.
Containerization is a way to run applications in an isolated environment called a container. Docker is a tool designed to make creating, deploying, and running applications using containers easier. Containers and Docker have become popular ways to package, deploy and run applications. They enable you to standardize the development workflow by codifying the instructions for building and packaging your applications.
Containers and Docker can be confusing to beginners. There are a lot of concepts to learn, and it can be challenging to see how these concepts fit together. In this course, I have summarized my years of experience automating development workflow with Docker so that you can learn the essential skills faster and become productive from day one. This course helps you learn faster by explaining the bigger picture and the problems they solve and focusing only the on the essential skills used in the development workflow.
Enroll now and begin the journey to becoming a more productive developer.