AI apps have gnarly dependencies: GPU drivers, CUDA, model weights, Python version conflicts. Docker solves all of it. This course covers Dockerfiles, Compose, AWS deployment, and CI/CD — built specifically for AI application stacks.
This is a text-first course that links out to the best supporting material on the internet instead of trying to replace it. The goal is to make this the best course on docker, containerization, and ai deployment you can find — even without producing a single minute of custom video.
This course is built by people who ship production docker, systems for a living. It reflects how things actually work on real projects — not how the documentation describes them.
Every day has working code snippets you can paste into your editor and run right now. The emphasis is on understanding what each line does, not memorizing syntax.
Instead of shooting videos that go stale in six months, Precision AI Academy links to the definitive open-source implementations, official documentation, and the best conference talks on the topic.
Each day is designed to finish in about an hour of focused reading plus hands-on work. You can do the whole course over a week of lunch breaks. No calendar commitment, no live classes, no quizzes.
Each day stands alone. Read them in order for the full picture, or jump straight to the day that answers the question you have today.
Images, containers, layers, and the Docker daemon. Why a container is not a VM, how the union filesystem works, and the first commands every developer needs to know.
Multi-stage builds, layer caching, managing Python dependencies, and the non-root user pattern that makes AI containers production-safe.
Running your FastAPI backend, PostgreSQL, Redis, and model inference server together with Compose. Networking, volumes, health checks, and env files.
Push your image to Elastic Container Registry and run it on App Runner with zero servers to manage. IAM roles, secrets, and the deployment commands.
Build, test, and push your Docker image on every push to main. Matrix builds, caching layers in GitHub Actions, and deployment triggers.
Instead of shooting our own videos, Precision AI Academy links to the best deep-dives already on YouTube. Watch them alongside the course. All external, all free, all from builders who ship this stuff.
From zero to running containers. Images, volumes, networking, and the mental model that makes Docker click.
Containerizing PyTorch, TensorFlow, and FastAPI AI inference servers. GPU passthrough, CUDA base images, and model weight management.
Building and pushing Docker images in GitHub Actions. Caching, secrets, and deployment to AWS and GCP.
Pushing Docker images to ECR and deploying them with App Runner. Zero-config scaling for containerized APIs.
The best way to understand any technology is to read the production-grade implementations that prove it works. These repositories implement patterns from every day of this course.
The source for Day 3. Understanding how Compose resolves service dependencies and manages network creation prevents a lot of debugging.
Multi-platform builds with BuildKit. Required for building ARM64 images on M1/M2 Macs that deploy to x86 AWS instances.
Official starter CI/CD workflows. The Docker publish workflow is the starting point for Day 5.
Production-grade FastAPI + Docker Compose template. Study it to understand how the experts structure multi-service AI apps.
Your model runs locally, fails in staging. Docker eliminates the environment mismatch. This course shows you how for AI-specific stacks.
You write APIs but deployment is still magic. This course demystifies containers and gives you a repeatable deployment process.
You trained a model. Now you need to deploy it. This course teaches you the containerization layer between Jupyter and production.
The 2-day in-person Precision AI Academy bootcamp covers Docker, containerization, and AI deployment hands-on. 5 U.S. cities. $1,490. 40 seats max. June–October 2026 (Thu–Fri).
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