section03.md

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

• Computer Vision using PyTorch

  • parameters.yml at conf/base/parameters.yml

  • Essential 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.yml

  • Essential 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)

• Computer Vision using OpenCV

  • parameters.yml at conf/base/parameters.yml
  • Essential 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.yml
  • Essential 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