Neurons.me is an open-source tool designed for creating and managing neural network models. It provides a user-friendly interface for building, training, and deploying deep learning models. With Neurons.me, users can easily experiment with different architectures, hyperparameters, and datasets to optimize their neural networks for various tasks. The tool simplifies the process of developing AI applications by abstracting away the complexities of model implementation and training.

LemonAI is a versatile machine learning library designed to simplify the process of building and deploying AI models. It provides a wide range of tools and algorithms for data preprocessing, model training, and evaluation. With LemonAI, users can easily experiment with different machine learning techniques and optimize their models for various tasks. The library is well-documented and beginner-friendly, making it suitable for both novice and experienced data scientists. LemonAI aims to streamline the development of AI applications and empower users to create innovative solutions using state-of-the-art machine learning methods.

Deepteam is a powerful open-source tool designed for deep learning projects. It provides a user-friendly interface for training, testing, and deploying deep neural networks. With Deepteam, users can easily create and manage complex models, visualize training progress, and optimize hyperparameters. The tool supports various deep learning frameworks and allows seamless integration with popular libraries like TensorFlow and PyTorch. Whether you are a beginner or an experienced deep learning practitioner, Deepteam simplifies the development process and accelerates model deployment.

Deeppowers is a powerful Python library for deep learning applications. It provides a wide range of tools and utilities to simplify the process of building and training deep neural networks. With Deeppowers, users can easily create complex neural network architectures, perform efficient training and optimization, and deploy models for various tasks. The library is designed to be user-friendly and flexible, making it suitable for both beginners and experienced deep learning practitioners.

AI_Spectrum is a versatile machine learning library that provides a wide range of tools and algorithms for building and deploying AI models. It offers a user-friendly interface for data preprocessing, model training, and evaluation. With AI_Spectrum, users can easily experiment with different machine learning techniques and optimize their models for various tasks. The library is designed to be flexible and scalable, making it suitable for both beginners and experienced data scientists.

pdr_ai_v2 is a Python library for implementing machine learning algorithms and models. It provides a wide range of tools and functionalities for data preprocessing, model training, evaluation, and deployment. The library is designed to be user-friendly and efficient, making it suitable for both beginners and experienced data scientists. With pdr_ai_v2, users can easily build and deploy machine learning models for various applications, such as classification, regression, clustering, and more.

Automodel is a Python library for automating the process of building and evaluating machine learning models. It provides a set of tools and utilities to streamline the model development workflow, from data preprocessing to model selection and evaluation. With Automodel, users can easily experiment with different algorithms, hyperparameters, and feature engineering techniques to find the best model for their dataset. The library is designed to be user-friendly and customizable, allowing users to define their own pipelines and workflows. Automodel is suitable for data scientists, machine learning engineers, and anyone looking to quickly build and test machine learning models without the need for manual intervention.

This repository contains a collection of AI algorithms and models for various machine learning tasks. It provides implementations of popular algorithms such as neural networks, decision trees, and support vector machines. The code is well-documented and easy to understand, making it suitable for both beginners and experienced developers. The repository also includes example datasets and tutorials to help users get started with building and training AI models. Whether you are a student learning about AI or a professional working on machine learning projects, this repository can be a valuable resource for your development journey.

ML-Retreat is a comprehensive machine learning library designed to simplify and streamline the process of building and deploying machine learning models. It provides a wide range of tools and utilities for data preprocessing, model training, evaluation, and deployment. With ML-Retreat, users can easily experiment with different algorithms, hyperparameters, and feature engineering techniques to optimize their models. The library is built with a focus on scalability, performance, and ease of use, making it suitable for both beginners and experienced machine learning practitioners.

BentoVLLM is an example project demonstrating how to serve and deploy open-source Large Language Models using vLLM, a high-throughput and memory-efficient inference engine. It provides a basis for advanced code customization, such as custom models, inference logic, or vLLM options. The project allows for simple LLM hosting with OpenAI compatible endpoints without the need to write any code. Users can interact with the server using Swagger UI or other methods, and the service can be deployed to BentoCloud for better management and scalability. Additionally, the repository includes integration examples for different LLM models and tools.

The Edge AI Libraries project is a collection of libraries, microservices, and tools for Edge application development. It includes sample applications showcasing generic AI use cases. Key components include Anomalib, Dataset Management Framework, Deep Learning Streamer, ECAT EnableKit, EtherCAT Masterstack, FLANN, OpenVINO toolkit, Audio Analyzer, ORB Extractor, PCL, PLCopen Servo, Real-time Data Agent, RTmotion, Audio Intelligence, Deep Learning Streamer Pipeline Server, Document Ingestion, Model Registry, Multimodal Embedding Serving, Time Series Analytics, Vector Retriever, Visual-Data Preparation, VLM Inference Serving, Intel Geti, Intel SceneScape, Visual Pipeline and Platform Evaluation Tool, Chat Question and Answer, Document Summarization, PLCopen Benchmark, PLCopen Databus, Video Search and Summarization, Isolation Forest Classifier, Random Forest Microservices. Visit sub-directories for instructions and guides.

Open AI is a powerful tool for artificial intelligence research and development. It provides a wide range of machine learning models and algorithms, making it easier for developers to create innovative AI applications. With Open AI, users can explore cutting-edge technologies such as natural language processing, computer vision, and reinforcement learning. The platform offers a user-friendly interface and comprehensive documentation to support users in building and deploying AI solutions. Whether you are a beginner or an experienced AI practitioner, Open AI offers the tools and resources you need to accelerate your AI projects and stay ahead in the rapidly evolving field of artificial intelligence.

AReaL (Ant Reasoning RL) is an open-source reinforcement learning system developed at the RL Lab, Ant Research. It is designed for training Large Reasoning Models (LRMs) in a fully open and inclusive manner. AReaL provides reproducible experiments for 1.5B and 7B LRMs, showcasing its scalability and performance across diverse computational budgets. The system follows an iterative training process to enhance model performance, with a focus on mathematical reasoning tasks. AReaL is equipped to adapt to different computational resource settings, enabling users to easily configure and launch training trials. Future plans include support for advanced models, optimizations for distributed training, and exploring research topics to enhance LRMs' reasoning capabilities.

FLAME is a lightweight and efficient deep learning framework designed for edge devices. It provides a simple and user-friendly interface for developing and deploying deep learning models on resource-constrained devices. With FLAME, users can easily build and optimize neural networks for tasks such as image classification, object detection, and natural language processing. The framework supports various neural network architectures and optimization techniques, making it suitable for a wide range of applications in the field of edge computing.

This repository provides a step-by-step guide for building your own machine learning models from scratch. It covers various machine learning algorithms and techniques, including linear regression, logistic regression, decision trees, and neural networks. The code examples are written in Python and include detailed explanations to help beginners understand the concepts behind machine learning. By following the tutorials in this repository, you can gain a deeper understanding of how machine learning works and develop your own models for different applications.

Bisheng is a leading open-source **large model application development platform** that empowers and accelerates the development and deployment of large model applications, helping users enter the next generation of application development with the best possible experience.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

Sorrentum is an open-source project that aims to combine open-source development, startups, and brilliant students to build machine learning, AI, and Web3 / DeFi protocols geared towards finance and economics. The project provides opportunities for internships, research assistantships, and development grants, as well as the chance to work on cutting-edge problems, learn about startups, write academic papers, and get internships and full-time positions at companies working on Sorrentum applications.

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

Mojo is a new programming language that bridges the gap between research and production by combining Python syntax and ecosystem with systems programming and metaprogramming features. Mojo is still young, but it is designed to become a superset of Python over time.

PandasAI is a Python library that makes it easy to ask questions to your data in natural language. It helps you to explore, clean, and analyze your data using generative AI.

Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.