bavard-ml-common 0.1.2

Machine learning model utilities

bavard-ml-common

A package of common code and utilities for machine learning and MLOps. Includes classes and methods for:

1. ML model serialization/deserialization
2. Google Cloud Storage IO operations
3. Converting an ML model into a runnable web service
4. Common ML model evaluation utilities
5. And more

Testing Locally

With Docker and docker-compose installed, run:

./scripts/lint-and-test-package.sh

Releasing The Package

Releasing the package is automatically handled by CI, but three steps must be taken to trigger a successful release:

1. Increment the VERSION variable in setup.py to the new desired version (e.g. VERSION="1.1.1")
2. Commit and tag the repo with the exact same value you populated the VERSION variable with (e.g. git tag 1.1.1)
3. Push the commit and tag to remote. These can be done together using: git push --atomic origin <branch name> <tag>

CI will then release the package to pypi with that version once the commit and tag are pushed.