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ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
A Python package to assess and improve fairness of machine learning models.
Low-code framework for building custom LLMs, neural networks, and other AI models
Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Open source platform for the machine learning lifecycle
Perform data science on data that remains in someone else's server
Model interpretability and understanding for PyTorch
Fit interpretable models. Explain blackbox machine learning.
Algorithms for explaining machine learning models
XAI - An eXplainability toolbox for machine learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
This is a collection of the code that accompanies the reports in The Gallery by Weights & Biases.
A model-agnostic visual debugging tool for machine learning
Python library assists deep learning on graphs
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, Materials Science and Biology
Graph convolutional neural network for multirelational link prediction
SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks (Scientific Reports)
PyRoss: inference, forecasts, and optimised control of epidemiological models in Python. github.com/rajeshrinet/pyross
pyebm - A toolbox for Event Based Models
An implementation of RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
π ππππππππππ PyTorch Implementation of DA-RNN (arXiv:1704.02971)
A Python sandbox for decision making in dynamics
A Python library that helps data scientists to infer causation rather than observing correlation.
Uplift modeling and causal inference with machine learning algorithms
A set of useful tools for competitive data science.