Getting Started with PipelineX¶
Kedro 0.17.x Starter projects¶
Kedro starters (Cookiecutter templates) to use Kedro, Scikit-learn, MLflow, and PipelineX are available at: kedro-starters-sklearn
Iris dataset is included and used, but you can easily change to Kaggle Titanic dataset.
Example/Demo Projects tested with Kedro 0.16.x¶
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parameters.yml
at conf/base/parameters.ymlEssential packages: PyTorch, Ignite, Shap, Kedro, MLflow
Application: Image classification
Data: MNIST images
Model: CNN (Convolutional Neural Network)
Loss: Cross-entropy
Kaggle competition using PyTorch
parameters.yml
at kaggle/conf/base/parameters.ymlEssential packages: PyTorch, Ignite, pandas, numpy, Kedro, MLflow
Application: Kaggle competition to predict the results of American Football plays
Data: Sparse heatmap-like field images and tabular data
Model: Combination of CNN and MLP
Loss: Continuous Rank Probability Score (CRPS)
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parameters.yml
at conf/base/parameters.ymlEssential packages: OpenCV, Scikit-image, numpy, TensorFlow (pretrained model), Kedro, MLflow
Application: Image processing to estimate the empty area ratio of cuboid container on a truck
Data: container images
Uplift Modeling using CausalLift
parameters.yml
at conf/base/parameters.ymlEssential packages: CausalLift, Scikit-learn, XGBoost, pandas, Kedro
Application: Uplift Modeling to find which customers should be targeted and which customers should not for a marketing campaign (treatment)
Data: generated by simulation