ml_observability_course

(β˜… 107)

Free Open-source ML observability course for data scientists and ML engineers. Learn how to monitor and debug your ML models in production.

File Explorer

Download Latest Version (.zip)
  • .gitbook.yaml
  • .gitignore
  • LICENSE
  • README.md

# Installation Guide

1. Get the code
git clone https://github.com/evidentlyai/ml_observability_course

Downloads the entire project code from GitHub to your computer.

cd ml_observability_course

Moves into the project folder you just downloaded.

2. Docker

Easy Recommended
Prerequisites
  • Git Needed to download the project code from GitHub.
  • Docker Desktop Needed to build and run containers. Install it and keep it running in the background.
⚠️ This is a large repository, so this method may point to an internal sub-package rather than the actual core product. Check the full README as well.
docker compose -f module5/airflow_conditional_checks/docker-compose.yaml up -d --build

Runs the command against the services defined in the compose file.

βœ… Run docker compose ps to check the containers are Up. If the README mentions a port, open http://localhost:PORT in your browser.

3. Python

Easy
Prerequisites
  • Git Needed to download the project code from GitHub.
  • Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.
pip install -r module2/requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

jupyter notebook

Launches Jupyter in your browser so you can open and run the notebook (.ipynb) files.

βœ… If it runs without errors and prints output in the terminal, it worked.

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