This open-source course teaches how to build an advanced RAG and LLM system using LLMOps and ML systems best practices. It helps you create an AI assistant that leverages your personal knowledge base to answer questions, summarize documents, and provide insights. The course covers topics such as LLM system architecture, pipeline orchestration, large-scale web crawling, model fine-tuning, and advanced RAG features. It is suitable for ML/AI engineers and data/software engineers & data scientists looking to level up to production AI systems. The course is free, with minimal costs for tools like OpenAI's API and Hugging Face's Dedicated Endpoints. Participants will build two separate Python applications for offline ML pipelines and online inference pipeline.

RAG Time is a 5-week AI learning series focusing on Retrieval-Augmented Generation (RAG) concepts. The repository contains code samples, step-by-step guides, and resources to help users master RAG. It aims to teach foundational and advanced RAG concepts, demonstrate real-world applications, and provide hands-on samples for practical implementation.

The LLM Twin Course is a free, end-to-end framework for building production-ready LLM systems. It teaches you how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices. The course is split into 11 hands-on written lessons and the open-source code you can access on GitHub. You can read everything and try out the code at your own pace.

Piccolo AI is an open-source software development toolkit for constructing sensor-based AI inference models optimized to run on low-power microcontrollers and IoT edge platforms. It includes SensiML's ML Engine, Embedded ML SDK, Analytic Studio UI, and SensiML Python Client. The tool is intended for individual developers, researchers, and AI enthusiasts, offering support for time-series sensor data classification and various applications such as acoustic event detection, activity recognition, gesture detection, anomaly detection, keyword spotting, and vibration classification.

OpenContracts is an Apache-2 licensed enterprise document analytics tool that supports multiple formats, including PDF and txt-based formats. It features multiple document ingestion pipelines with a pluggable architecture for easy format and ingestion engine support. Users can create custom document analytics tools with beautiful result displays, support mass document data extraction with a LlamaIndex wrapper, and manage document collections, layout parsing, automatic vector embeddings, and human annotation. The tool also offers pluggable parsing pipelines, human annotation interface, LlamaIndex integration, data extraction capabilities, and custom data extract pipelines for bulk document querying.

Refly.AI is an open-source AI-native creation engine that empowers users to transform ideas into production-ready content. It features a free-form canvas interface with multi-threaded conversations, knowledge base integration, contextual memory, intelligent search, WYSIWYG AI editor, and more. Users can leverage AI-powered capabilities, context memory, knowledge base integration, quotes, and AI document editing to enhance their content creation process. Refly offers both cloud and self-hosting options, making it suitable for individuals, enterprises, and organizations. The tool is designed to facilitate human-AI collaboration and streamline content creation workflows.

KubeSphere is a distributed operating system for cloud-native application management, using Kubernetes as its kernel. It provides a plug-and-play architecture, allowing third-party applications to be seamlessly integrated into its ecosystem. KubeSphere is also a multi-tenant container platform with full-stack automated IT operation and streamlined DevOps workflows. It provides developer-friendly wizard web UI, helping enterprises to build out a more robust and feature-rich platform, which includes most common functionalities needed for enterprise Kubernetes strategy.

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

Instructa AI Prompts is an open-source repository dedicated to collecting and sharing AI prompts, best practices, and curated rules for developers. The goal is to help users quickly set up and refine their workflow with ready-to-use prompts. Users can dynamically include prompts in AI-assisted coding tools like Cursor, GitHub Copilot, Zed, Windsurf, and Cline to adhere to project-specific coding standards, best practices, and automation workflows.

Co-op Translator is a tool designed to facilitate communication between team members working on cooperative projects. It allows users to easily translate messages and documents in real-time, enabling seamless collaboration across language barriers. The tool supports multiple languages and provides accurate translations to ensure clear and effective communication within the team. With Co-op Translator, users can improve efficiency, productivity, and teamwork in their cooperative endeavors.

Phi Cookbook is a repository containing hands-on examples with Microsoft's Phi models, which are a series of open source AI models developed by Microsoft. Phi is currently the most powerful and cost-effective small language model with benchmarks in various scenarios like multi-language, reasoning, text/chat generation, coding, images, audio, and more. Users can deploy Phi to the cloud or edge devices to build generative AI applications with limited computing power.

OmAgent is an open-source agent framework designed to streamline the development of on-device multimodal agents. It enables agents to empower various hardware devices, integrates speed-optimized SOTA multimodal models, provides SOTA multimodal agent algorithms, and focuses on optimizing the end-to-end computing pipeline for real-time user interaction experience. Key features include easy connection to diverse devices, scalability, flexibility, and workflow orchestration. The architecture emphasizes graph-based workflow orchestration, native multimodality, and device-centricity, allowing developers to create bespoke intelligent agent programs.

The .NET AI Workshop is a comprehensive guide designed to help developers add AI features to .NET applications. It covers various AI-based features such as classification, summarization, data extraction/cleaning, anomaly detection, translation, sentiment detection, semantic search, Q&A chatbots, and voice assistants. The workshop is tailored for developers new to AI in .NET applications, focusing on the usage of AI services without the need for prior AI technology knowledge. It provides examples using .NET and C# with a focus on AI topics, aiming to enhance productivity and automation in applications.

Yuna AI is a unique AI companion designed to form a genuine connection with users. It runs exclusively on the local machine, ensuring privacy and security. The project offers features like text generation, language translation, creative content writing, roleplaying, and informal question answering. The repository provides comprehensive setup and usage guides for Yuna AI, along with additional resources and tools to enhance the user experience.

Raycast-G4F is a free extension that allows users to leverage powerful AI models such as GPT-4 and Llama-3 within the Raycast app without the need for an API key. The extension offers features like streaming support, diverse commands, chat interaction with AI, web search capabilities, file upload functionality, image generation, and custom AI commands. Users can easily install the extension from the source code and benefit from frequent updates and a user-friendly interface. Raycast-G4F supports various providers and models, each with different capabilities and performance ratings, ensuring a versatile AI experience for users.

The AI-Gateway repository explores the AI Gateway pattern through a series of experimental labs, focusing on Azure API Management for handling AI services APIs. The labs provide step-by-step instructions using Jupyter notebooks with Python scripts, Bicep files, and APIM policies. The goal is to accelerate experimentation of advanced use cases and pave the way for further innovation in the rapidly evolving field of AI. The repository also includes a Mock Server to mimic the behavior of the OpenAI API for testing and development purposes.

AIHub is a client that integrates the capabilities of multiple large models, allowing users to quickly and easily build their own personalized AI assistants. It supports custom plugins for endless possibilities. The tool provides powerful AI capabilities, rich configuration options, customization of AI assistants for text and image conversations, AI drawing, installation of custom plugins, personal knowledge base building, AI calendar generation, support for AI mini programs, and ongoing development of additional features. Users can download the application package from the release section, resolve issues related to macOS app installation, and contribute ideas by submitting issues. The project development involves installation, development, and building processes for different operating systems.

jvm-openai is a minimalistic unofficial OpenAI API client for the JVM, written in Java. It serves as a Java client for OpenAI API with a focus on simplicity and minimal dependencies. The tool provides support for various OpenAI APIs and endpoints, including Audio, Chat, Embeddings, Fine-tuning, Batch, Files, Uploads, Images, Models, Moderations, Assistants, Threads, Messages, Runs, Run Steps, Vector Stores, Vector Store Files, Vector Store File Batches, Invites, Users, Projects, Project Users, Project Service Accounts, Project API Keys, and Audit Logs. Users can easily integrate this tool into their Java projects to interact with OpenAI services efficiently.

This open-source course teaches how to build an advanced RAG and LLM system using LLMOps and ML systems best practices. It helps you create an AI assistant that leverages your personal knowledge base to answer questions, summarize documents, and provide insights. The course covers topics such as LLM system architecture, pipeline orchestration, large-scale web crawling, model fine-tuning, and advanced RAG features. It is suitable for ML/AI engineers and data/software engineers & data scientists looking to level up to production AI systems. The course is free, with minimal costs for tools like OpenAI's API and Hugging Face's Dedicated Endpoints. Participants will build two separate Python applications for offline ML pipelines and online inference pipeline.

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

Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a set of AI libraries for simplifying ML compute, including Data, Train, Tune, RLlib, and Serve. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations. With Ray, you can seamlessly scale the same code from a laptop to a cluster, making it easy to meet the compute-intensive demands of modern ML workloads.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and allows users to integrate machine learning and deep learning models with their Java applications. The framework is deep learning engine agnostic, enabling users to switch engines at any point for optimal performance. DJL's ergonomic API interface guides users with best practices to accomplish deep learning tasks, such as running inference and training neural networks.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are: * `MLflow Tracking `_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects `_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models `_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry `_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

This open-source course teaches how to build an advanced RAG and LLM system using LLMOps and ML systems best practices. It helps you create an AI assistant that leverages your personal knowledge base to answer questions, summarize documents, and provide insights. The course covers topics such as LLM system architecture, pipeline orchestration, large-scale web crawling, model fine-tuning, and advanced RAG features. It is suitable for ML/AI engineers and data/software engineers & data scientists looking to level up to production AI systems. The course is free, with minimal costs for tools like OpenAI's API and Hugging Face's Dedicated Endpoints. Participants will build two separate Python applications for offline ML pipelines and online inference pipeline.

Knavigator is a project designed to analyze, optimize, and compare scheduling systems, with a focus on AI/ML workloads. It addresses various needs, including testing, troubleshooting, benchmarking, chaos engineering, performance analysis, and optimization. Knavigator interfaces with Kubernetes clusters to manage tasks such as manipulating with Kubernetes objects, evaluating PromQL queries, as well as executing specific operations. It can operate both outside and inside a Kubernetes cluster, leveraging the Kubernetes API for task management. To facilitate large-scale experiments without the overhead of running actual user workloads, Knavigator utilizes KWOK for creating virtual nodes in extensive clusters.

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.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

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.