#jupyter_notebook #machine_learning #deep_learning #gradient_boosting #interpretability #shapley #shap #explainability

https://github.com/slundberg/shap

#jupyter_notebook #ethical_artificial_intelligence #explainability #explainable_ml #interpretability #lime #machine_learning #python #shap #transparency

https://github.com/MAIF/shapash

#other #awesome #awesome_list #data_mining #deep_learning #explainability #interpretability #large_scale_machine_learning #large_scale_ml #machine_learning #machine_learning_operations #ml_operations #ml_ops #mlops #privacy_preserving #privacy_preserving_machine_learning #privacy_preserving_ml #production_machine_learning #production_ml #responsible_ai

This repository provides a comprehensive list of open-source libraries and tools for deploying, monitoring, versioning, scaling, and securing machine learning models in production. Here are the key benefits The repository includes a wide range of tools categorized into sections such as adversarial robustness, agentic workflow, AutoML, computation load distribution, data labelling and synthesis, data pipelines, data storage optimization, data stream processing, deployment and serving, evaluation and monitoring, explainability and fairness, feature stores, and more.

- **Production Readiness** The repository is actively maintained and contributed to by a community of developers, ensuring that the tools are up-to-date and well-supported.

- **Ease of Use** Tools for optimized computation, model storage optimization, and neural search and retrieval help in improving the performance and efficiency of machine learning models.

- **Privacy and Security**: Libraries focused on privacy and security, such as federated learning and homomorphic encryption, ensure that sensitive data is protected during model training and deployment.

Using this repository, you can streamline your machine learning workflows, improve model performance, and ensure robustness and security in your production environments.

https://github.com/EthicalML/awesome-production-machine-learning