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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
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
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
Yellowbrick is a machine learning visualization library and model diagnostic tool designed to analyze feature importance, target distributions, and model error metrics. It serves as a visual toolkit for diagnosing underfitting and overfitting through the use of validation and learning curves. The project provides specialized suites for evaluating predictive models and unsupervised learning. It enables the determination of optimal cluster counts via elbow methods and silhouette coefficients, and assesses classifier and regressor quality through ROC curves, confusion matrices, and residual plot
A Lightweight Library for AI Observability
The main features of cfahlgren1/observers are: Observability And Monitoring.
Projects with overlapping indexed features include: decodingai-magazine/llm-twin-course — This project is an educational curriculum and set of technical guides for building production-ready large language… seldonio/seldon-core — Seldon Core is a Kubernetes-based machine learning model server and MLOps inference framework. It functions as a… paddlepaddle/fastdeploy — FastDeploy is a high-performance deployment framework for large language models, vision models, and multimodal models.… helicone/helicone — Helicone is an AI gateway and observability platform designed to intercept, manage, and monitor interactions with… 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…