Awesome Data Analysis
Section: Tools · Tool for analyzing and monitoring data and model drift.
Entry
Appears in 8 awesome lists
Interactive reports to analyze machine learning models during validation or production monitoring.
Section: Tools · Tool for analyzing and monitoring data and model drift.
Section: Visualization · Interactive reports to analyze machine learning models during validation or production monitoring.
Section: Observability · An open-source framework to evaluate, test and monitor ML and LLM-powered systems.
Section: Python · Interactive reports to analyze machine learning models during validation or production monitoring.
Section: Visual Analysis and Debugging · Interactive reports to analyze ML models during validation or production monitoring.
Section: 8. MLOps / LLMOps & Production · ML & LLM monitoring framework.
Section: Evaluation and Monitoring · Evidently is an open-source framework to evaluate, test and monitor ML and LLM-powered systems.
Section: Data Validation · Evaluate and monitor ML models from validation to production.
Unified proxy and SDK that routes to 100+ LLM providers behind a single OpenAI-compatible interface, with a Router handling retry/fallback across deployments, per-project cost and rate-limit tracking, and OTEL callback integrations. The right infrastructure layer when your harness needs provider…
Comet's open-source AI observability and evaluation platform: deep tracing of LLM calls, conversation logging, and agent activity, plus built-in eval metrics, prompt versioning, guardrails, and the Opik Agent Optimizer. Worth including because it unifies observability, verification, and…
(from Hpcaitech) - A Unified Deep Learning System for Large-Scale Parallel Training (1D, 2D, 2.5D, 3D and sequence parallelism, and ZeRO protocol).
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search,…
State-of-the-art serving engine with PagedAttention and continuous batching. Currently the fastest production-grade LLM server.
MindsDB is an Explainable AutoML framework for developers. With MindsDB you can build, train and use state of the art ML models in as simple as one line of code.
LLM engineering platform for model tracing, prompt management, and application evaluation. Langfuse helps teams collaboratively debug, analyze, and iterate on their LLM applications such as chatbots or AI agents. (Demo, Source Code, Clients) MIT Docker
Validation & testing of machine learning models and data during model development, deployment, and production. This includes checks and suites related to various types of issues, such as model performance, data integrity, distribution mismatches, and more.