Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It provides a centralized environment for tracing execution flows, managing prompt templates, and monitoring production performance, allowing teams to gain visibility into complex model interactions and tool usage without requiring manual application code changes. The platform distinguishes itself through its integrated approach to the AI development lifecycle, combining distributed trace instrumentation with automated evaluation frameworks. It supports model-as-a-judge scoring, syn
FastDeploy is a high-performance deployment framework for large language models, vision models, and multimodal models. It provides the infrastructure to launch model services that process combined image, video, and text inputs, exposing these capabilities through a standardized, OpenAI-compatible API for chat and text completions. The project distinguishes itself through advanced inference pipeline engineering and GPU optimization. It employs speculative decoding, tensor parallelism, and a disaggregated execution model that separates prefill and decode phases across different hardware resourc
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
Seldon Core is a Kubernetes-based machine learning model server and MLOps inference framework. It functions as a multi-model serving engine and pipeline orchestrator, packaging models as scalable microservices that are exposed via standardized REST and gRPC APIs. The project distinguishes itself through graph-based inference pipelines that chain models and data transformers into sequential workflows. It optimizes hardware utilization via multi-model shared serving and dynamic memory overcommit strategies, while supporting production experimentation through weighted traffic routing, A/B testin
The testing platform for AI teams. Bring engineers, PMs, and domain experts together to generate tests, simulate (adversarial) conversations, and trace every failure to its root cause.
Die Hauptfunktionen von rhesis-ai/rhesis sind: Observability And Monitoring, Developer Playgrounds.
Open-Source-Alternativen zu rhesis-ai/rhesis sind unter anderem: comet-ml/opik — Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It… seldonio/seldon-core — Seldon Core is a Kubernetes-based machine learning model server and MLOps inference framework. It functions as a… decodingai-magazine/llm-twin-course — This project is an educational curriculum and set of technical guides for building production-ready large language… paddlepaddle/fastdeploy — FastDeploy is a high-performance deployment framework for large language models, vision models, and multimodal models.… evidentlyai/evidently — Evidently is an AI observability platform and evaluation framework designed to quantify the performance of machine… districtdatalabs/yellowbrick — Yellowbrick is a machine learning visualization library and model diagnostic tool designed to analyze feature…