cintel-06-continuous-intelligence

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 brings together several techniques used in continuous intelligence systems.

The goal is to copy this repository, set up your environment, run the example analysis, and explore how monitoring techniques can be combined to assess the current state of a system.

Run the example pipeline, read the code, and see how:

• raw system metrics are transformed into useful signals
• anomalies are detected in those signals
• monitoring results are summarized to assess system health

This project demonstrates how monitoring data can support operational awareness and decision-making.

This module serves as a capstone: it encourages you to combine techniques developed in earlier modules into a simple continuous intelligence pipeline:

• Module 2. anomaly detection
• Module 3. signal design
• Module 4. rolling monitoring
• Module 5. drift comparison
• Module 6. system assessment (integration).

Data

The example pipeline reads system metrics from:

data/system_metrics_case.csv

Each row represents one observation of system activity.

The pipeline derives signals such as error rate and average latency, checks for anomalous conditions, and produces a summary assessment of system behavior.

The dataset includes a short period of degraded performance so that monitoring signals and anomaly detection produce visible results.

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/brandon112123/cintel-06-continuous-intelligence

cd cintel-06-continuous-intelligence
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
# repeat if changes were made
git add -A
uvx pre-commit run --all-files

uv run python -m cintel.continuous_intelligence_case
uv run python -m cintel.continuous_intelligence_brandon
uv run python -m cintel.continuous_intelligence_custom

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.

My Modification

I created my own version of the continuous intelligence pipeline by copying the example file and renaming it from continuous_intelligence_case.py to continuous_intelligence_brandon.py.

I made a simple technical modification by changing the monitoring thresholds:

• MAX_ERROR_RATE from 0.05 to 0.04
• MAX_AVG_LATENCY from 40.0 to 35.0

I also changed the data file and output file names to my own versions:

data/system_metrics_brandon.csv artifacts/system_assessment_brandon.csv

I made this change so the pipeline would use stricter monitoring settings and run from my own custom data and output files.

Custom Project

I created a custom continuous intelligence project using continuous_intelligence_custom.py.

This project reads system data from data/system_metrics_custom.csv and creates several monitoring signals, including error rate, average latency, rolling latency, health score, status, and latency trend.

The project produces three main outputs:

• artifacts/system_monitoring_details_custom.csv
• artifacts/system_assessment_custom.csv
• artifacts/system_health_chart_custom.png

I changed MAX_ERROR_RATE from 0.05 to 0.03 and changed MAX_AVG_LATENCY from 40.0 to 30.0 to make the system more sensitive to warning conditions.

This modification helped turn the example project into a more complete monitoring system by adding row level evaluation, rolling analysis, trend tracking, and a chart for easier interpretation.