Open-source LLM evaluation and red teaming framework. Test prompts, models, agents, and RAG pipelines. Run adversarial attacks (jailbreaks, prompt injection) and integrate security testing into CI/CD.
Ambrosia helps you clean up your LLM datasets using other LLMs.
Aqueduct enables you to easily define, run, and manage AI & ML tasks on any cloud infrastructure.
Ready to use deeplearning docker images.
Version and deploy your ML models following GitOps principles
ML powered analytics engine for outlier/anomaly detection and root cause analysis.
A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with metrics and graphs in pull/merge requests. Framework & language agnostic.
A tool that allows the conversion of ML models into native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart) with zero dependencies.
Python tool to help you configure, organize, log and reproduce experiments. Like a notebook lab in the context of Chemistry/Biology. The community has built multiple add-ons leveraging the proposed standard.
a lightweight library to define data transformations as a directed-acyclic graph (DAG). It helps author reliable feature engineering and machine learning pipelines, and more.
Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models.
Python library for experiment metrics logging into simply formatted local files.
Data Science Version Control is an open-source version control system for machine learning projects with pipelines support. It makes ML projects reproducible and shareable.
Deepnote is a drop-in replacement for Jupyter with an AI-first design, sleek UI, new blocks, and native data integrations. Use Python, R, and SQL locally in your favorite IDE, then scale to Deepnote cloud for real-time collaboration, Deepnote agent, and deployable data apps.
A starter kit for Jupyter notebooks and machine learning. Companion docker images consist of all combinations of python versions, machine learning frameworks (Keras, PyTorch and Tensorflow) and CPU/CUDA versions.
All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a docker container and is preloaded with a variety of popular data science libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code).
Build semantic search applications and workflows.
The AI-native database built for LLM applications, providing incredibly fast vector and full-text search. Developed using C++20
A list of all things related to TensorFlow.
A curated list of awesome Keras projects, libraries and resources.
On-device streaming speech recognition SDK for iOS with Swift bindings (SPM). Based on NVIDIA NeMo FastConformer (80 ms cache-aware lookahead). Companion Silero VAD, wake-word, and 14-command KWS via same runtime.
A curated list of machine learning models in CoreML format.
A curated list of pretrained CoreML models.
A library for machine learning that builds predictions using a linear regression.
Swift Language Bindings of TensorFlow. Using native TensorFlow models on both macOS / Linux.
The first neural network / machine learning library written in Swift. This is a project for AI algorithms in Swift for iOS and OS X development. This project includes algorithms focused on Bayes theorem, neural networks, SVMs, Matrices, etc...
A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.
A toolbox framework of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians.
A bare bones library that includes a general matrix language and wraps some OpenCV for iOS development. [Deprecated]
The iOS and OS X neural network framework.
a next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems design, and beyond.
Highly optimized artificial intelligence and machine learning library written in Swift.
Fast Neural Networks framework built on top of Metal. Supports TensorFlow models.
Neural network inference from the command line, implemented in CHICKEN Scheme.
A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy cross-platform inference.
Strongly-typed Scala API for TensorFlow.
An in-memory machine learning library built on top of Breeze. It provides immutable objects and exposes its functionality through a scikit-learn-like API.
Simply written algorithms to help study ML or write your own implementations.
Flexible Declarative Learning-Based Programming.
Scala Library/REPL for Machine Learning Research.
H2O and Spark interoperability.
a Scala library for constructing probabilistic models.
CPU and GPU-accelerated Machine Learning Library.
Bioinformatics for the Scala programming language
A genomics processing engine and specialized file format built using Apache Avro, Apache Spark and Parquet. Apache 2 licensed.
Scalding powered machine learning. [Deprecated]
Distributed decision tree ensemble learning in Scala.
Scalable Machine Learning in Scalding.