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Facets is a project containing two visualizations, Facets Overview and Facets Dive, for understanding and analyzing machine learning datasets. The visualizations are Polymer web components backed by TypeScript that can be embedded in Jupyter notebooks or webpages. Overview produces feature-by-feature statistical analysis and compares statistics across datasets. The repository was archived on Jul 27, 2024 and is now read-only.
Fairlearn is a Python package hosted on a GitHub repository maintained by the Fairlearn project, designed to assess and improve the fairness of machine learning models. The repository includes documentation covering fairness definitions and a getting started guide for the tool.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Fcoijmefliggikpeofhojmkpoooocifk provides machine-learning models, research, training resources, or evaluation tools.
Feast is an end-to-end open source feature store for machine learning. It allows teams to define, manage, discover, and serve features, delivering structured data to AI and LLM applications during training and inference. Documented use cases include real-time recommendations, fraud detection, risk scoring, and customer segmentation.
Feast is an open source feature store for machine learning. It manages an offline store for historical data, a low-latency online store, and a feature server. It generates point-in-time correct feature sets to avoid data leakage and provides a single data access layer abstracting feature storage from retrieval. Installable with pip and initialized through a CLI.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Feature Stores for ML is a global community of data science professionals, researchers, and engineers focused on feature stores for machine learning pipelines. It creates educational content about feature stores and the surrounding data and AI ecosystem, hosts monthly industry meetups, and provides resources including blog posts, videos, benchmarks, and an MLOps dictionary.
Set of tools for creating and testing machine learning features, with a scikit-learn compatible API.
FEDOT is an open-source framework for automated modeling and machine learning. It provides automatic generative design of machine learning pipelines, with an evolutionary approach supporting classification, regression, clustering and time series prediction. Pipelines are represented as graphs defining connections between data preprocessing and model blocks. It is distributed under the 3-Clause BSD license.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
A highly-modular C++ machine learning library for embedded electronics and robotics.
Flama is a production framework for predictive and generative AI. It enables serving any machine learning model as an API with a single line of code, and offers OpenAI, Anthropic, and Ollama-compatible endpoints, a built-in chat user interface, and native MCP support.
FLAML is a lightweight Python library from Microsoft for automating machine learning and AI operations. It performs model selection and hyperparameter optimization under resource constraints, including tuning foundation model inference hyperparameters. It requires Python 3.10 or later, is installable via pip, and can be used as a scikit-learn style estimator.
FlexML is an AutoML library for Python that assists in building machine learning models by automating model selection and hyperparameter tuning. It supports regression and classification tasks and offers quick and wide experiment modes for choosing how many models to train.
Flight Fare Prediction is a GitHub repository that implements a machine learning project for predicting flight fares. The repository includes documentation such as a problem statement and topics related to the prediction task.
Floode provides machine-learning models, research, training resources, or evaluation tools.
Flux is a machine learning library built as a pure-Julia stack, offering lightweight abstractions over Julia's native GPU and automatic differentiation support. It is compiled and eager, and existing Julia libraries can be incorporated into Flux models. Its ecosystem includes probabilistic programming, graph neural networks, computer vision and natural language processing packages, and it is maintained by a community team.
An open source Python library for drift detection in machine learning systems.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Gaussian Mixture Model is an unsupervised machine learning implementation using a multivariate Gaussian mixture model. It supports both offline data and real-time data streams. The repository provides installation instructions, a simple example, and a license. It is hosted on GitHub at https://github.com/lukapopijac/gaussian-mixture-model.
Generative AI Takes Stereotypes and Bias From Bad to Worse provides machine-learning models, research, training resources, or evaluation tools.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
