reidfalconer

styleguide

Style guides for Google-originated open-source projects

airflow

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

stats

macOS system monitor in your menu bar

memray

Memray is a memory profiler for Python

kedro

Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

Pytorch-UNet

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

metaflow

:rocket: Build and manage real-life ML, AI, and data science projects with ease!

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

causalml

Uplift modeling and causal inference with machine learning algorithms

EconML

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

python-causality-handbook

Causal Inference for the Brave and True. A light-hearted yet rigorous approach to learning about impact estimation and causality.

Ax

Adaptive Experimentation Platform

SDV

Synthetic data generation for tabular data

causalnex

A Python library that helps data scientists to infer causation rather than observing correlation.

nixtla

TimeGPT-1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.

orbit

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

scikit-uplift

:exclamation: uplift modeling in scikit-learn style in python :snake:

OpenMLOps

causallib

A Python package for modular causal inference analysis and model evaluations

python-geospatial

A collection of Python packages for geospatial analysis with binder-ready notebook examples

FastTreeSHAP

Fast SHAP value computation for interpreting tree-based models

production-data-science

Production Data Science: a workflow for collaborative data science aimed at production

sentinel2-cloud-detector

Sentinel Hub Cloud Detector for Sentinel-2 images in Python

upliftml

UpliftML: A Python Package for Scalable Uplift Modeling

detectree

Tree detection from aerial imagery in Python

pycrop-yield-prediction

A PyTorch Implementation of Jiaxuan You's Deep Gaussian Process for Crop Yield Prediction

justcause

💊 Comparing causality methods in a fair and just way.

judgyprophet

Forecasting for knowable future events using Bayesian informative priors (forecasting with judgmental-adjustment).

MLOps

pearl-ml-pipeline