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Netron is a visualizer for neural network and machine learning models. It provides a graphical interface that renders model architectures as interactive node-link diagrams, allowing users to inspect internal layers, tensors, and metadata. By performing static analysis, the tool enables the examination of model definitions without executing the underlying machine learning code. The software distinguishes itself through a schema-driven parsing engine that translates diverse proprietary model formats into a unified internal graph structure. This approach ensures interoperability, allowing users
Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques required to craft effective inputs and improve the quality of generative AI outputs. The project covers advanced prompting strategies to enhance reasoning, reliability, and output quality. This includes techniques for task decomposition, chain-of-thought reasoning, and the use of few-shot and zero-shot guidance. It also addresses model security through the study of prompt hacking, vulnerability analysis, and privacy auditing to prevent sensitive data leaks. The scope extends to th
This project is an educational curriculum and set of technical guides for building production-ready large language model and retrieval augmented generation systems. It provides instructional materials and hands-on lessons focused on model specialization, LLMOps, and the implementation of vector databases. The course covers the development of retrieval augmented generation systems, including tutorials on creating data pipelines that crawl, chunk, and embed content into vector stores. It includes training guides for the deployment, monitoring, and maintenance of language models in production en
FATE is an open-source federated learning platform that enables multiple organizations to collaboratively train machine learning models without exposing raw data to any party. It provides a complete framework for private data collaboration, allowing participants to jointly compute on sensitive information while maintaining data privacy and security guarantees through secure multi-party computation protocols. The platform distinguishes itself through its comprehensive infrastructure management capabilities, supporting automated deployment of multi-party clusters using Ansible-driven provisioni
A model-agnostic visual debugging tool for machine learning
The main features of uber/manifold are: AI Security and Red Teaming, Model Visualization, Observability And Monitoring.
Projects with overlapping indexed features include: lutzroeder/netron — Netron is a visualizer for neural network and machine learning models. It provides a graphical interface that renders… trigaten/learn_prompting — Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques… seldonio/seldon-core — Seldon Core is a Kubernetes-based machine learning model server and MLOps inference framework. It functions as a… federatedai/fate — FATE is an open-source federated learning platform that enables multiple organizations to collaboratively train… paddlepaddle/fastdeploy — FastDeploy is a high-performance deployment framework for large language models, vision models, and multimodal models.… decodingai-magazine/llm-twin-course — This project is an educational curriculum and set of technical guides for building production-ready large language…