e2e-ml-with-open-source

Overview

This Kedro project was showcased at PyCon Ireland in November 2024 to demonstrate the integration of MLOps tools, including Kedro, MLflow, and Airflow. The demo ML pipeline addresses a common ML problem: collecting and preprocessing data from multiple sources, training and evaluating a model, and deploying it.

Steps

• Install dependencies from requirements.txt.
• Set up the Kedro VS Code extension to visualize your pipelines in the IDE. Kedro VS Code Extension
• Use kedro run to execute and test your pipeline locally.
• Install kedro-mlflow to track artifacts and runs, and to leverage the model registry. Kedro-MLflow Documentation
• Install kedro-airflow or explore other deployment plugins to convert and deploy your pipeline to different platforms. Kedro-Airflow Documentation | Deployment Plugins

Rules and guidelines

In order to get the best out of the template:

• Don't remove any lines from the .gitignore file we provide
• Make sure your results can be reproduced by following a data engineering convention
• Don't commit data to your repository
• Don't commit any credentials or your local configuration to your repository. Keep all your credentials and local configuration in conf/local/

How to install dependencies

Declare any dependencies in requirements.txt for pip installation.

To install them, run:

pip install -r requirements.txt

How to run your Kedro pipeline

You can run your Kedro project with:

kedro run

How to test your Kedro project

Have a look at the files src/tests/test_run.py and src/tests/pipelines/data_science/test_pipeline.py for instructions on how to write your tests. Run the tests as follows:

pytest

To configure the coverage threshold, look at the .coveragerc file.

Project dependencies

To see and update the dependency requirements for your project use requirements.txt. You can install the project requirements with pip install -r requirements.txt.

Further information about project dependencies

How to work with Kedro and notebooks

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: catalog, context, pipelines and session.

Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run pip install -r requirements.txt you will not need to take any extra steps before you use them.

Jupyter

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

JupyterLab

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

IPython

And if you want to run an IPython session:

kedro ipython

How to ignore notebook output cells in git

To automatically strip out all output cell contents before committing to git, you can use tools like nbstripout. For example, you can add a hook in .git/config with nbstripout --install. This will run nbstripout before anything is committed to git.

Note: Your output cells will be retained locally.

Package your Kedro project

Further information about building project documentation and packaging your project