This repo contains links to all official and community created example Kedro projects as well as the complete version of the spaceflights tutorial.
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We recommend starting with
kedro-tutorial, which is the complete version of the Space Flights tutorial described in the Kedro documentation and includes the data necessary to run the project. Some of the old code examples are available in the following links:
The Kedro-Training repository contains documentation that you can use to deliver Kedro training.
These repositories contain the official Kedro tutorial projects, also known as starters:
Our community of Kedro users are creating their own profile projects, you can have a look at:
- Response Recommendation System for BarefootLaw by Kasun Amarasinghe, Carlos Caro, Nupoor Gandhi and Raphaelle Roffo, an extensive Data Science for Social Good (DSSG) at Imperial College London project that recommends responses to law related queries
- Augury by Craig Franklin, machine-learning functionality for predicting AFL match results in the Tipresias app
- CausalLift by Yusuke Minami, a Python package for Uplift Modeling in real-world business
- PipelineX by Yusuke Minami, a Python package to develop pipelines for rapid Machine/Deep Learning experimentation using Kedro and MLflow. Example projects using PyTorch, Pandas, and OpenCV are available.
- kedro-mlflow-example by Tom Goldenberg, a project that demonstrates how to integrate MLflow with a Kedro codebase
Kedro is licensed under the Apache 2.0 License.