Awesome Opensource Ai

Time series forecasting with PyTorch

Lightning ⚡️ fast forecasting with statistical and econometric models.

On-device AI across mobile, embedded and edge for PyTorch

An Engine-Agnostic Deep Learning Framework in Java

An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

Relax! Flux is the ML library that doesn't make you tensor

A Rust machine learning framework.

Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research

A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.

AdalFlow: The library to build & auto-optimize LLM applications.

Machine Learning Pipelines for Kubeflow

Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test your data and models from research to production.

A retargetable MLIR-based machine learning compiler and runtime toolkit.

Maestro: Netflix’s Workflow Orchestrator

The official CLI and Python client for the Hugging Face Hub.

Superfast AI decision making and intelligent processing of multi-modal data.

AI Infra / AI Orchestration / AI Control Plane

Synthetic data generation for tabular data

Evaluation and Tracking for LLM Experiments and AI Agents

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

JAX-based neural network library

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Visualize and compare datasets, target values and associations, with one line of code.

Open-source deep-learning framework for building, training, and fine-tuning deep learning models using state-of-the-art Physics-ML methods

Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).

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. https://deepnote.com/

Data manipulation and transformation for audio signal processing, powered by PyTorch

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

A library for debugging/inspecting machine learning classifiers and explaining their predictions

Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

🤗 Evaluate: A library for easily evaluating machine learning models and datasets.

Machine learning metrics for distributed, scalable PyTorch applications.

SDG is a specialized framework designed to generate high-quality structured tabular data.

A Python package to assess and improve fairness of machine learning models.

Feature engineering and selection open-source Python library compatible with sklearn.

Vendor-agnostic orchestration for training, inference and agentic workloads across NVIDIA, AMD, TPU, and Tenstorrent on clouds, Kubernetes, and bare metal.

TFX is an end-to-end platform for deploying production ML pipelines

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

Build LLM-powered applications in Ruby

A Julia machine learning framework

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Interpretability and explainability of data and machine learning models

Large-scale LLM inference engine

Reference implementations of MLPerf® training benchmarks

JVector: the most advanced embedded vector search engine

Synthetic data curation for post-training and structured data extraction

OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.

Automated Machine Learning on Kubernetes

MLRun is an open source MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications.