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Hands-On Lab: AI-Assisted Dockerfile Optimization for Python Projects

Audit, prioritize, and fix a production-grade Dockerfile using structured analysis and AI-assisted tooling. Walk away with a measurably smaller, faster, and more secure container image.
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By completing this lab, you will be able to:

  • Measure Docker image metrics including build time, disk usage, and layer structure
  • Identify Dockerfile antipatterns related to image size, layer caching, and security
  • Use AI tools to generate and prioritize a structured issue audit matrix
  • Implement a production-hardened Dockerfile with a pinned base image, non-root user, and optimized layer order
  • Benchmark an optimized image and document measurable improvements
What you are going to learn

From Bloated to Production-Ready: A Structured Approach to Dockerfile Optimization

This lab puts you in the role of a developer who has inherited a working but poorly built Dockerfile for a Python Flask API. You will start by capturing a baseline: build time, image size, and layer structure. Then you will audit the Dockerfile yourself, use an AI assistant to expand and validate your findings, and build a prioritized issue matrix before writing a single line of improved code.

By the end of the lab, you will have implemented a production-hardened Dockerfile and benchmarked it against your original baseline. You will leave with a structured workflow for auditing and optimizing Docker images that transfers to any project, along with hands-on practice using AI tools to surface issues, prioritize fixes, and accelerate implementation.

Recommended Courses

The following courses are highly recommended before tackling this lab. They will provide all the knowledge you need to understand everything that is discussed in the lab. That being said, if you are already familiar with Docker and its fundamentals (images, containers, build context, Dockerfiles, etc.), feel free to jump in right away!

Lab Contents