cintel-05-drift-detection

Professional Python project for continuous intelligence.

Continuous intelligence systems monitor data streams, detect change, and respond in real time. This course builds those capabilities through working projects.

In the age of generative AI, durable skills are grounded in real work: setting up a professional environment, reading and running code, understanding the logic, and pushing work to a shared repository. Each project follows the structure of professional Python projects. We learn by doing.

This Project

This project introduces drift detection.

In practice, drift detection often begins with baseline comparison, comparing current system behavior to an earlier reference period.

The goal is to copy this repository, set up your environment, run the example analysis, and explore how system behavior can change between an earlier reference period and a more recent current period.

Run the example pipeline, read the code, and make small modifications to understand:

• how average metrics are compared
• how baseline differences can indicate system change
• how simple drift flags can be created

This approach provides a basic foundation for detecting changes in system behavior.

Data

The example pipeline reads system metrics from:

data/reference_metrics_case.csv data/current_metrics_case.csv

Each row represents one observation of system behavior. The pipeline compares the reference and current datasets, summarizes average values, and saves the drift results as an artifact.

Working Files

You'll work with just these areas:

• data/ - it starts with the data
• docs/ - tell the story
• src/cintel/ - where the magic happens
• pyproject.toml - update authorship & links
• zensical.toml - update authorship & links

Instructions

Follow the step-by-step workflow guide to complete:

1. Phase 1. Start & Run
2. Phase 2. Change Authorship
3. Phase 3. Read & Understand
4. Phase 4. Modify
5. Phase 5. Apply

Challenges

Challenges are expected. Sometimes instructions may not quite match your operating system. When issues occur, share screenshots, error messages, and details about what you tried. Working through issues is part of implementing professional projects.

Success

After completing Phase 1. Start & Run, you'll have your own GitHub project, running on your machine, and running the example will print out:

========================
Pipeline executed successfully!
========================

And a new file named project.log will appear in the project folder.

Command Reference

The commands below are used in the workflow guide above. They are provided here for convenience.

Follow the guide for the full instructions.

Show command reference

In a machine terminal (open in your Repos folder)

After you get a copy of this repo in your own GitHub account, open a machine terminal in your Repos folder:

# Replace username with YOUR GitHub username.
git clone https://github.com/username/cintel-05-drift-detection

cd cintel-05-drift-detection
code .

In a VS Code terminal

uv self update
uv python pin 3.14
uv sync --extra dev --extra docs --upgrade

uvx pre-commit install
git add -A
uvx pre-commit run --all-files

uv run python -m cintel.case_drift_detector

uv run ruff format .
uv run ruff check . --fix
uv run zensical build

git add -A
git commit -m "update"
git push -u origin main

Notes

• Use the UP ARROW and DOWN ARROW in the terminal to scroll through past commands.
• Use CTRL+f to find (and replace) text within a file.