shap
awesome-production-machine-learning
shap | awesome-production-machine-learning | |
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1 | 9 | |
20,121 | 16,178 | |
- | 2.3% | |
10.0 | 7.5 | |
8 months ago | 3 days ago | |
Jupyter Notebook | ||
MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
shap
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Ethical and Bias Testing in Generative AI: A Practical Guide to Ensuring Ethical Conduct with Test Cases and Tools
Other tools like Fairness Indicators, Lime, and SHAP are also valuable resources for ethical and bias testing.
awesome-production-machine-learning
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
One trove of treasures is the awesome-production-machine-learning repository on GitHub. This curated list provides a multitude of frameworks, libraries, and software designed to facilitate various stages of the ML lifecycle.
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
There is a cool, gigantic list for MLOps that I can recommend: https://github.com/EthicalML/awesome-production-machine-learning
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How much of a full DS project pipeline can I do for free?
There are a lot of frameworks and specific tools out there that try to make production ML projects viable; from specific like Airflow (orchestrating jobs) and MLflow (experiment tracking) to more complex ones like Kubeflow. You can have a grasp here.
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Sqldiff: SQLite Database Difference Utility
https://github.com/EthicalML/awesome-production-machine-lear...
- [D] What are the best resources to crack M L system design interviews?
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I'm looking for a tool that let's you visualize the models architecture like this. Any idea what it is called?
https://github.com/EthicalML/awesome-production-machine-learning I think you will find most of the tools to visualize the model on this link.
- Awesome production machine learning - curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning [free] [website] [@all]
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Crucial differences in MLOps for deep learning
2/ https://github.com/EthicalML/awesome-production-machine-learning
What are some alternatives?
csgo-impact-rating - A probabilistic player rating system for Counter Strike: Global Offensive, powered by machine learning
shap - A game theoretic approach to explain the output of any machine learning model.
transformers-interpret - Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
awesome-jax - JAX - A curated list of resources https://github.com/google/jax
lime - Lime: Explaining the predictions of any machine learning classifier
netron - Visualizer for neural network, deep learning and machine learning models
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
awesome-mlops - :sunglasses: A curated list of awesome MLOps tools
augmented-interpretable-models - Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
awesome-ml-for-cybersecurity - :octocat: Machine Learning for Cyber Security
datascience - Curated list of Python resources for data science.