Runbooks is a repository that is no longer active. The project has been deprecated in favor of KubeAI, a platform designed to simplify the operationalization of AI on Kubernetes. For more information, please refer to the new repository at https://github.com/substratusai/kubeai.

The 'ai-demos' repository is a collection of example code from presentations focusing on building with AI and LLMs. It serves as a resource for developers looking to explore practical applications of artificial intelligence in their projects. The code snippets showcase various techniques and approaches to leverage AI technologies effectively. The repository aims to inspire and educate developers on integrating AI solutions into their applications.

MaiBot is an intelligent QQ group chat bot based on a large language model. It is developed using the nonebot2 framework, with LLM providing conversation abilities, MongoDB for data persistence support, and NapCat as the QQ protocol endpoint support. The project is in active development stage, with features like chat functionality, emoji functionality, schedule management, memory function, knowledge base function, and relationship function planned for future updates. The project aims to create a 'life form' active in QQ group chats, focusing on companionship and creating a more human-like presence rather than a perfect assistant. The application generates content from AI models, so users are advised to discern carefully and not use it for illegal purposes.

Aishare is a collaborative platform for sharing AI models and datasets. It allows users to upload, download, and explore various AI models and datasets. Users can also rate and comment on the shared resources, providing valuable feedback to the community. Aishare aims to foster collaboration and knowledge sharing in the field of artificial intelligence.

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.

Disciplined AI Software Development is a comprehensive repository that provides guidelines and best practices for developing AI software in a disciplined manner. It covers topics such as project organization, code structure, documentation, testing, and deployment strategies to ensure the reliability, scalability, and maintainability of AI applications. The repository aims to help developers and teams navigate the complexities of AI development by offering practical advice and examples to follow.

OpenInference is a set of conventions and plugins that complement OpenTelemetry to enable tracing of AI applications. It provides a way to capture and analyze the performance and behavior of AI models, including their interactions with other components of the application. OpenInference is designed to be language-agnostic and can be used with any OpenTelemetry-compatible backend. It includes a set of instrumentations for popular machine learning SDKs and frameworks, making it easy to add tracing to your AI applications.

The Ramalama project simplifies working with AI by utilizing OCI containers. It automatically detects GPU support, pulls necessary software in a container, and runs AI models. Users can list, pull, run, and serve models easily. The tool aims to support various GPUs and platforms in the future, making AI setup hassle-free.

Azure AI Docs is a repository that provides detailed documentation and resources for developers looking to leverage Microsoft's AI services on the Azure platform. The repository covers a wide range of topics including machine learning, natural language processing, computer vision, and more. Developers can find tutorials, code samples, best practices, and guidelines to help them integrate AI capabilities into their applications seamlessly.

LangDB AI Gateway is an open-source enterprise AI gateway built in Rust. It provides a unified interface to all LLMs using the OpenAI API format, focusing on high performance, enterprise readiness, and data control. The gateway offers features like comprehensive usage analytics, cost tracking, rate limiting, data ownership, and detailed logging. It supports various LLM providers and provides OpenAI-compatible endpoints for chat completions, model listing, embeddings generation, and image generation. Users can configure advanced settings, such as rate limiting, cost control, dynamic model routing, and observability with OpenTelemetry tracing. The gateway can be run with Docker Compose and integrated with MCP tools for server communication.

Awesome-AI-Security is a curated list of resources for AI security, including tools, research papers, articles, and tutorials. It aims to provide a comprehensive overview of the latest developments in securing AI systems and preventing vulnerabilities. The repository covers topics such as adversarial attacks, privacy protection, model robustness, and secure deployment of AI applications. Whether you are a researcher, developer, or security professional, this collection of resources will help you stay informed and up-to-date in the rapidly evolving field of AI security.

OpenCopilot is a tool that allows users to create their own AI copilot for their products. It integrates with APIs to execute calls as needed, using LLMs to determine the appropriate endpoint and payload. Users can define API actions, validate schemas, and integrate a user-friendly chat bubble into their SaaS app. The tool is capable of calling APIs, transforming responses, and populating request fields based on context. It is not suitable for handling large APIs without JSON transformers. Users can teach the copilot via flows and embed it in their app with minimal code.

vLLM Ascend plugin is a backend plugin designed to run vLLM on the Ascend NPU. It provides a hardware-pluggable interface that allows popular open-source models to run seamlessly on the Ascend NPU. The plugin is recommended within the vLLM community and adheres to the principles of hardware pluggability outlined in the RFC. Users can set up their environment with specific hardware and software prerequisites to utilize this plugin effectively.

Zoonk is a web app designed for creating interactive courses using AI. Currently in early development stage, it is not yet ready for use but aims to be available for testing and contributions in the future. The project focuses on leveraging AI technology to enhance the learning experience by providing interactive course creation tools. Zoonk also conducts model evaluations on different prompts to improve its AI capabilities. The project has garnered support from various individuals who believe in its vision and potential.

llama.ui is an open-source desktop application that provides a beautiful, user-friendly interface for interacting with large language models powered by llama.cpp. It is designed for simplicity and privacy, allowing users to chat with powerful quantized models on their local machine without the need for cloud services. The project offers multi-provider support, conversation management with indexedDB storage, rich UI components including markdown rendering and file attachments, advanced features like PWA support and customizable generation parameters, and is privacy-focused with all data stored locally in the browser.

Purple Llama is an umbrella project that aims to provide tools and evaluations to support responsible development and usage of generative AI models. It encompasses components for cybersecurity and input/output safeguards, with plans to expand in the future. The project emphasizes a collaborative approach, borrowing the concept of purple teaming from cybersecurity, to address potential risks and challenges posed by generative AI. Components within Purple Llama are licensed permissively to foster community collaboration and standardize the development of trust and safety tools for generative AI.

Runbooks is a repository that is no longer active. The project has been deprecated in favor of KubeAI, a platform designed to simplify the operationalization of AI on Kubernetes. For more information, please refer to the new repository at https://github.com/substratusai/kubeai.

Katib is a Kubernetes-native project for automated machine learning (AutoML). Katib supports Hyperparameter Tuning, Early Stopping and Neural Architecture Search. Katib is the project which is agnostic to machine learning (ML) frameworks. It can tune hyperparameters of applications written in any language of the users’ choice and natively supports many ML frameworks, such as TensorFlow, Apache MXNet, PyTorch, XGBoost, and others. Katib can perform training jobs using any Kubernetes Custom Resources with out of the box support for Kubeflow Training Operator, Argo Workflows, Tekton Pipelines and many more.

Cog is an open-source tool that lets you package machine learning models in a standard, production-ready container. You can deploy your packaged model to your own infrastructure, or to Replicate.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

WebLLM is a modular and customizable javascript package that directly brings language model chats directly onto web browsers with hardware acceleration. Everything runs inside the browser with no server support and is accelerated with WebGPU. WebLLM is fully compatible with OpenAI API. That is, you can use the same OpenAI API on any open source models locally, with functionalities including json-mode, function-calling, streaming, etc. We can bring a lot of fun opportunities to build AI assistants for everyone and enable privacy while enjoying GPU acceleration.

The Data Scientist Roadmap2024 provides a comprehensive guide to mastering essential tools for data science success. It includes programming languages, machine learning libraries, cloud platforms, and concepts categorized by difficulty. The roadmap covers a wide range of topics from programming languages to machine learning techniques, data visualization tools, and DevOps/MLOps tools. It also includes web development frameworks and specific concepts like supervised and unsupervised learning, NLP, deep learning, reinforcement learning, and statistics. Additionally, it delves into DevOps tools like Airflow and MLFlow, data visualization tools like Tableau and Matplotlib, and other topics such as ETL processes, optimization algorithms, and financial modeling.

AI By Doing is a hands-on artificial intelligence tutorial series that aims to help beginners understand the principles of machine learning and deep learning while providing practical applications. The content covers various supervised and unsupervised learning algorithms, machine learning engineering, deep learning fundamentals, frameworks like TensorFlow and PyTorch, and applications in computer vision and natural language processing. The tutorials are written in Jupyter Notebook format, combining theory, mathematical derivations, and Python code implementations to facilitate learning and understanding.

Ultimate Data Science Toolkit is a comprehensive repository covering Python basics to Generative AI. It includes modules on Python programming, data analysis, statistics, machine learning, MLOps, case studies, and deep learning. The repository provides detailed tutorials on various topics such as Python data structures, control statements, functions, modules, object-oriented programming, exception handling, file handling, web API, databases, list comprehension, lambda functions, Pandas, Numpy, data visualization, statistical analysis, supervised and unsupervised machine learning algorithms, model serialization, ML pipeline orchestration, case studies, and deep learning concepts like neural networks and autoencoders.

Runbooks is a repository that is no longer active. The project has been deprecated in favor of KubeAI, a platform designed to simplify the operationalization of AI on Kubernetes. For more information, please refer to the new repository at https://github.com/substratusai/kubeai.

AIOps Modules is a collection of reusable Infrastructure as Code (IAC) modules that work with SeedFarmer CLI. The modules are decoupled and can be aggregated using GitOps principles to achieve desired use cases, removing heavy lifting for end users. They must be generic for reuse in Machine Learning and Foundation Model Operations domain, adhering to SeedFarmer Guide structure. The repository includes deployment steps, project manifests, and various modules for SageMaker, Mlflow, FMOps/LLMOps, MWAA, Step Functions, EKS, and example use cases. It also supports Industry Data Framework (IDF) and Autonomous Driving Data Framework (ADDF) Modules.

Awesome-LLMOps is a curated list of the best LLMOps tools, providing a comprehensive collection of frameworks and tools for building, deploying, and managing large language models (LLMs) and AI agents. The repository includes a wide range of tools for tasks such as building multimodal AI agents, fine-tuning models, orchestrating applications, evaluating models, and serving models for inference. It covers various aspects of the machine learning operations (MLOps) lifecycle, from training to deployment and observability. The tools listed in this repository cater to the needs of developers, data scientists, and machine learning engineers working with large language models and AI applications.

Skyflo.ai is an AI agent designed for Cloud Native operations, providing seamless infrastructure management through natural language interactions. It serves as a safety-first co-pilot with a human-in-the-loop design. The tool offers flexible deployment options for both production and local Kubernetes environments, supporting various LLM providers and self-hosted models. Users can explore the architecture of Skyflo.ai and contribute to its development following the provided guidelines and Code of Conduct. The community engagement includes Discord, Twitter, YouTube, and GitHub Discussions.

AI+CloudOps is a cloud-native operations management platform designed for enterprises. It aims to integrate artificial intelligence technology with cloud-native practices to significantly improve the efficiency and level of operations work. The platform offers features such as AIOps for monitoring data analysis and alerts, multi-dimensional permission management, visual CMDB for resource management, efficient ticketing system, deep integration with Prometheus for real-time monitoring, and unified Kubernetes management for cluster optimization.

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.