AWESOME DATA SCIENCE
Section: Miscellaneous Tools · Game theoretic approach to explain the output of any machine learning model
Entry
Appears in 4 awesome lists
A game theoretic approach to explain the output of any machine learning model.
Section: Miscellaneous Tools · Game theoretic approach to explain the output of any machine learning model
Section: Model Interpretability · A game theoretic approach to explain the output of any machine learning model.
Section: Model Explanation · A unified approach to explain the output of any machine learning model.
Section: Repositories · A Python module for using Shapley Additive Explanations.
Comet's open-source AI observability and evaluation platform: deep tracing of LLM calls, conversation logging, and agent activity, plus built-in eval metrics, prompt versioning, guardrails, and the Opik Agent Optimizer. Worth including because it unifies observability, verification, and…
Visualizer for deep learning and machine learning models (no Python code, but visualizes models from most Python Deep Learning frameworks).
"Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it…
A fast and simple framework for building and running distributed applications. Ray is packaged with RLlib, a scalable reinforcement learning library, and Tune, a scalable hyperparameter tuning library. ray.io
Build and share delightful machine learning apps, all in Python. The de facto standard for creating interactive ML demos with automatic UI generation from function signatures. Powers thousands of Hugging Face Spaces.
Workflow management system that makes it easy to take your data pipelines and add semantics like retries, logging, dynamic mapping, caching, failure notifications, and more.