Model Mondays is a repository dedicated to providing a collection of machine learning models implemented in Python. It aims to serve as a resource for individuals looking to explore and experiment with various machine learning algorithms and techniques. The repository includes a wide range of models, from simple linear regression to complex deep learning architectures, along with detailed documentation and examples to facilitate learning and understanding. Whether you are a beginner looking to get started with machine learning or an experienced practitioner seeking reference implementations, Model Mondays offers a valuable repository of models to study and leverage in your projects.

LightLLM is a lightweight library for linear and logistic regression models. It provides a simple and efficient way to train and deploy machine learning models for regression tasks. The library is designed to be easy to use and integrate into existing projects, making it suitable for both beginners and experienced data scientists. With LightLLM, users can quickly build and evaluate regression models using a variety of algorithms and hyperparameters. The library also supports feature engineering and model interpretation, allowing users to gain insights from their data and make informed decisions based on the model predictions.

SciML.ai is an open source software organization dedicated to unifying packages for scientific machine learning. It focuses on developing modular scientific simulation support software, including differential equation solvers, inverse problems methodologies, and automated model discovery. The organization aims to provide a diverse set of tools with a common interface, creating a modular, easily-extendable, and highly performant ecosystem for scientific simulations. The website serves as a platform to showcase SciML organization's packages and share news within the ecosystem. Pull requests are encouraged for contributions.

AI Engineer Headquarters is a comprehensive learning resource designed to help individuals master scientific methods, processes, algorithms, and systems to build stories and models in the field of Data and AI. The repository provides in-depth content through video sessions and text materials, catering to individuals aspiring to be in the top 1% of Data and AI experts. It covers various topics such as AI engineering foundations, large language models, retrieval-augmented generation, fine-tuning LLMs, reinforcement learning, ethical AI, agentic workflows, and career acceleration. The learning approach emphasizes action-oriented drills and routines, encouraging consistent effort and dedication to excel in the AI field.

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.

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.

Chatmcp is a chatbot framework for building conversational AI applications. It provides a flexible and extensible platform for creating chatbots that can interact with users in a natural language. With Chatmcp, developers can easily integrate chatbot functionality into their applications, enabling users to communicate with the system through text-based conversations. The framework supports various natural language processing techniques and allows for the customization of chatbot behavior and responses. Chatmcp simplifies the development of chatbots by providing a set of pre-built components and tools that streamline the creation process. Whether you are building a customer support chatbot, a virtual assistant, or a chat-based game, Chatmcp offers the necessary features and capabilities to bring your conversational AI ideas to life.

This repository is the workshop repository for the Causal Modeling in Machine Learning Workshop on Altdeep.ai. The material is open source and free. The course covers causality in model-based machine learning, Bayesian modeling, interventions, counterfactual reasoning, and deep causal latent variable models. It aims to equip learners with the ability to build causal reasoning algorithms into decision-making systems in data science and machine learning teams within top-tier technology organizations.

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.

RLinf is a flexible and scalable open-source infrastructure designed for post-training foundation models via reinforcement learning. It provides a robust backbone for next-generation training, supporting open-ended learning, continuous generalization, and limitless possibilities in intelligence development. The tool offers unique features like Macro-to-Micro Flow, flexible execution modes, auto-scheduling strategy, embodied agent support, and fast adaptation for mainstream VLA models. RLinf is fast with hybrid mode and automatic online scaling strategy, achieving significant throughput improvement and efficiency. It is also flexible and easy to use with multiple backend integrations, adaptive communication, and built-in support for popular RL methods. The roadmap includes system-level enhancements and application-level extensions to support various training scenarios and models. Users can get started with complete documentation, quickstart guides, key design principles, example gallery, advanced features, and guidelines for extending the framework. Contributions are welcome, and users are encouraged to cite the GitHub repository and acknowledge the broader open-source community.

DSPy.rb is a Ruby framework for building reliable LLM applications using composable, type-safe modules. It enables developers to define typed signatures and compose them into pipelines, offering a more structured approach compared to traditional prompting. The framework embraces Ruby conventions and adds innovations like CodeAct agents and enhanced production instrumentation, resulting in scalable LLM applications that are robust and efficient. DSPy.rb is actively developed, with a focus on stability and real-world feedback through the 0.x series before reaching a stable v1.0 API.

Agent Lightning is a lightweight and efficient tool for automating repetitive tasks in the field of data analysis and machine learning. It provides a user-friendly interface to create and manage automated workflows, allowing users to easily schedule and execute data processing, model training, and evaluation tasks. With its intuitive design and powerful features, Agent Lightning streamlines the process of building and deploying machine learning models, making it ideal for data scientists, machine learning engineers, and AI enthusiasts looking to boost their productivity and efficiency in their projects.

Metaflow is a user-friendly library designed to assist scientists and engineers in developing and managing real-world data science projects. Initially created at Netflix, Metaflow aimed to enhance the productivity of data scientists working on diverse projects ranging from traditional statistics to cutting-edge deep learning. For further information, refer to Metaflow's website and documentation.

The Local Assistant Examples repository is a collection of educational examples showcasing the use of large language models (LLMs). It was initially created for a blog post on building a RAG model locally, and has since expanded to include more examples and educational material. Each example is housed in its own folder with a dedicated README providing instructions on how to run it. The repository is designed to be simple and educational, not for production use.

This repository provides a tool for fetching and summarizing daily papers from the arXiv repository. It allows users to stay updated with the latest research in various fields by automatically retrieving and summarizing papers on a daily basis. The tool simplifies the process of accessing and digesting academic papers, making it easier for researchers and enthusiasts to keep track of new developments in their areas of interest.

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.