Useful Resources

Here’s a list of some useful resources.

Libraries

We’ve seen some of the following libraries in this course. Here's a review of their functions.

• OmegaConf: Manages hierarchical configuration files.

• argparse: Parses command-line arguments.

• logging: Logs messages for debugging and maintenance purposes.

• pytest: Unit tests our code.

• pytest-cov: Checks code coverage after unit testing our code.

• Papermill: Runs parameterized Jupyter Notebooks programmatically.

Data quality checking

• Great Expectations: A platform that allows us to check if the data input to our model meets our expectations or specifications.

Dependency management and packaging

• Poetry: A tool to manage dependencies and create packages.

Documentation

• MkDocs: A modern documentation system.

• Sphinx: Another documentation system, more popular than MkDocs.

ML frameworks

The following frameworks are useful for modeling and deployment:

• Kedro: A pipeline framework.

• MLflow: A framework for modeling, experiment tracking, and deployment.

• Metaflow: A framework for modeling and deployment.

Miscellaneous

The following miscellaneous resources may be useful to the Python or ML developer:

• Google Python Style Guide: A popular Python style guide developed by Google.

• Git: A modern, distributed version control system.