The capstone ties the whole course together into one project. You don't need every piece — even one clean, working, end-to-end flow is enough to be interview-ready.
Every module in this course builds one capability. The capstone assembles them:
log analysis -> 03-file-handling-and-logs + 04-object-oriented-python
|
run it as a CLI -> 05-cli-tools-argparse
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pull in cloud data -> 06-aws-automation-boto3
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expose it as an API -> 07-apis-with-fastapi
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add an AI layer -> 08-ai-agents-for-devops (optional, impressive)
Pick a scope and make it clean, commented, and runnable:
- Minimum: the log analyzer as a CLI tool (
05-cli-tools-argparse) that writes a JSON summary. - Solid: wrap the log analyzer + system metrics in a FastAPI service
(
07-apis-with-fastapi) with/health,/logs,/metrics. - Impressive: add the AWS report endpoint and/or the local AI log agent.
The reference implementation of the "Solid + AWS" tier already lives in
../07-apis-with-fastapi/devops-utilities-api.
Use it as your starting point, or build your own.
- A working project (one clean flow is enough).
- A project-level
README.mdexplaining what it does and how to run it. - A STAR explanation (see below) you can use in interviews.
Fill in STAR.md:
- Situation — what was the problem? (e.g. "logs grew daily; manual checks were slow")
- Task — what were you responsible for? (e.g. "automate log analysis in Python")
- Action — what did you actually do? (parse logs, CLI, API, AI summary)
- Result — what was the outcome? (faster visibility, less manual effort)
- Code runs without errors from a clean
venv -
README.mdexplains setup + run steps - Functions are named clearly and commented where needed
- Errors are handled (missing file, bad input, failed API call)
-
STAR.mdwritten and rehearsed
CI/CD, Kubernetes, monitoring, cloud architecture — Python for DevOps was your foundation. Keep going: DevOps – Zero To Hero.