Ml

llmfit is a CLI/web tool that inspects your machine's CPU, RAM, and GPU/VRAM, then scores which local LLMs will actually run well on that hardware by memory fit, speed, quality, and context length.

A 2023 Medium article comparing the text detection and recognition performance of four open-source OCR engines: Tesseract, EasyOCR, PaddleOCR, and MMOCR.

A Hugging Face Space by user Loren for comparing OCR output from EasyOCR, PaddleOCR, MMOCR, and Tesseract on an uploaded image.

Aequitas is an open-source bias and fairness audit toolkit for machine learning models, developed by the Center for Data Science and Public Policy at the University of Chicago. Its docs cover installing the Python package, using its CLI, and reading the fairness metrics it generates across subgroups to support equitable decision-making.

Alibi is an open-source Python library for machine learning model inspection and interpretation, maintained by SeldonIO. It implements local and global explanation methods such as Anchor, Counterfactual, Integrated Gradients, and ALE for tabular, text, and image data.

Deepchecks is an open-source platform for validating machine learning data and models, from tabular data to NLP and computer vision. It groups individual validation checks into suites covering data integrity, distribution drift, and model performance.

DeepMind Lab is a 3D research environment built on the Quake III Arena engine, used as a testbed for reinforcement learning agents on navigation and puzzle-solving tasks.

Fairlearn is an open-source Python toolkit for assessing and improving fairness in machine learning systems. It pairs an interactive dashboard with unfairness-mitigation algorithms for navigating trade-offs between fairness and model performance.

Great Expectations is an open-source Python framework for validating, profiling, and documenting data quality. Assertions about a dataset, called Expectations, are expressed as human-readable Python methods and run against data sources ranging from CSV files to databases.

MLflow is an open-source platform for the machine learning and AI application lifecycle, covering experiment tracking, model versioning, evaluation, and deployment. It is backed by the Linux Foundation and integrates with over 100 tools across the ML ecosystem.

TensorFlow's guide to TensorFlow Model Analysis (TFMA), a library for evaluating TensorFlow models at scale within a TFX pipeline. It computes metrics over data slices and can visualize the results in a Jupyter notebook.

WhyLabs, an AI observability platform for monitoring machine learning models and LLM applications, discontinued operations in 2026. Its full platform was open-sourced, and its whylogs and LangKit tools continue on as open-source projects.

Yellowbrick is a Python library of diagnostic visualizations that extends scikit-learn's API to help steer feature engineering, algorithm selection, and hyperparameter tuning during model selection.

Kaggle's dataset hub, where individuals, research groups, and organizations publish and share open datasets for machine learning and data analysis. Listings typically include descriptions, data dictionaries, and discussion threads.