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.

Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, Tensorflow, XGBoost, MPI, Paddle and others. Training Operator allows you to use Kubernetes workloads to effectively train your large models via Kubernetes Custom Resources APIs or using Training Operator Python SDK. > Note: Before v1.2 release, Kubeflow Training Operator only supports TFJob on Kubernetes. * For a complete reference of the custom resource definitions, please refer to the API Definition. * TensorFlow API Definition * PyTorch API Definition * Apache MXNet API Definition * XGBoost API Definition * MPI API Definition * PaddlePaddle API Definition * For details of all-in-one operator design, please refer to the All-in-one Kubeflow Training Operator * For details on its observability, please refer to the monitoring design doc.

Nous is an open-source TypeScript platform for autonomous AI agents and LLM based workflows. It aims to automate processes, support requests, review code, assist with refactorings, and more. The platform supports various integrations, multiple LLMs/services, CLI and web interface, human-in-the-loop interactions, flexible deployment options, observability with OpenTelemetry tracing, and specific agents for code editing, software engineering, and code review. It offers advanced features like reasoning/planning, memory and function call history, hierarchical task decomposition, and control-loop function calling options. Nous is designed to be a flexible platform for the TypeScript community to expand and support different use cases and integrations.

Sophia is an open-source TypeScript platform designed for autonomous AI agents and LLM based workflows. It aims to automate processes, review code, assist with refactorings, and support various integrations. The platform offers features like advanced autonomous agents, reasoning/planning inspired by Google's Self-Discover paper, memory and function call history, adaptive iterative planning, and more. Sophia supports multiple LLMs/services, CLI and web interface, human-in-the-loop interactions, flexible deployment options, observability with OpenTelemetry tracing, and specific agents for code editing, software engineering, and code review. It provides a flexible platform for the TypeScript community to expand and support various use cases and integrations.

Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.

Open Assistant API is an open-source, self-hosted AI intelligent assistant API compatible with the official OpenAI interface. It supports integration with more commercial and private models, R2R RAG engine, internet search, custom functions, built-in tools, code interpreter, multimodal support, LLM support, and message streaming output. Users can deploy the service locally and expand existing features. The API provides user isolation based on tokens for SaaS deployment requirements and allows integration of various tools to enhance its capability to connect with the external world.

HumanLayer is a Python toolkit designed to enable AI agents to interact with humans in tool-based and asynchronous workflows. By incorporating humans-in-the-loop, agentic tools can access more powerful and meaningful tasks. The toolkit provides features like requiring human approval for function calls, human as a tool for contacting humans, omni-channel contact capabilities, granular routing, and support for various LLMs and orchestration frameworks. HumanLayer aims to ensure human oversight of high-stakes function calls, making AI agents more reliable and safe in executing impactful tasks.

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.

Burr is a Python library and UI that makes it easy to develop applications that make decisions based on state (chatbots, agents, simulations, etc...). Burr includes a UI that can track/monitor those decisions in real time.

AdalFlow is a library designed to help developers build and optimize Large Language Model (LLM) task pipelines. It follows a design pattern similar to PyTorch, offering a light, modular, and robust codebase. Named in honor of Ada Lovelace, AdalFlow aims to inspire more women to enter the AI field. The library is tailored for various GenAI applications like chatbots, translation, summarization, code generation, and autonomous agents, as well as classical NLP tasks such as text classification and named entity recognition. AdalFlow emphasizes modularity, robustness, and readability to support users in customizing and iterating code for their specific use cases.

MaixCDK (Maix C/CPP Development Kit) is a C/C++ development kit that integrates practical functions such as AI, machine vision, and IoT. It provides easy-to-use encapsulation for quickly building projects in vision, artificial intelligence, IoT, robotics, industrial cameras, and more. It supports hardware-accelerated execution of AI models, common vision algorithms, OpenCV, and interfaces for peripheral operations. MaixCDK offers cross-platform support, easy-to-use API, simple environment setup, online debugging, and a complete ecosystem including MaixPy and MaixVision. Supported devices include Sipeed MaixCAM, Sipeed MaixCAM-Pro, and partial support for Common Linux.

EvalKit is an open-source TypeScript library for evaluating and improving the performance of large language models (LLMs). It helps developers ensure the reliability, accuracy, and trustworthiness of their AI models. The library provides various metrics such as Bias Detection, Coherence, Faithfulness, Hallucination, Intent Detection, and Semantic Similarity. EvalKit is designed to be user-friendly with detailed documentation, tutorials, and recipes for different use cases and LLM providers. It requires Node.js 18+ and an OpenAI API Key for installation and usage. Contributions from the community are welcome under the Apache 2.0 License.

Cloudberry Database (CBDB or CloudberryDB) is a next-generation unified database for analytics and AI. It is created by a bunch of original Greenplum Database developers and ASF committers. Cloudberry Database aims to bring modern computing capabilities to the traditional distributed MPP database to support Analytics and AI/ML workloads in one platform.

LangKit is an open-source text metrics toolkit for monitoring language models. It offers methods for extracting signals from input/output text, compatible with whylogs. Features include text quality, relevance, security, sentiment, toxicity analysis. Installation via PyPI. Modules contain UDFs for whylogs. Benchmarks show throughput on AWS instances. FAQs available.

Reflection-Tuning is a project focused on improving the quality of instruction-tuning data through a reflection-based method. It introduces Selective Reflection-Tuning, where the student model can decide whether to accept the improvements made by the teacher model. The project aims to generate high-quality instruction-response pairs by defining specific criteria for the oracle model to follow and respond to. It also evaluates the efficacy and relevance of instruction-response pairs using the r-IFD metric. The project provides code for reflection and selection processes, along with data and model weights for both V1 and V2 methods.

AgentBench is a benchmark designed to evaluate Large Language Models (LLMs) as autonomous agents in various environments. It includes 8 distinct environments such as Operating System, Database, Knowledge Graph, Digital Card Game, and Lateral Thinking Puzzles. The tool provides a comprehensive evaluation of LLMs' ability to operate as agents by offering Dev and Test sets for each environment. Users can quickly start using the tool by following the provided steps, configuring the agent, starting task servers, and assigning tasks. AgentBench aims to bridge the gap between LLMs' proficiency as agents and their practical usability.

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.

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.

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.

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.

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.

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.