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Evidently

Appears in 8 awesome lists

Interactive reports to analyze machine learning models during validation or production monitoring.

Open github.comevidentlyai/evidently

Found in these lists

Awesome Data Analysis

Section: Tools · Tool for analyzing and monitoring data and model drift.

FreshScore 80

Awesome Jupyter

Section: Visualization · Interactive reports to analyze machine learning models during validation or production monitoring.

FreshScore 88

Awesome LLMOps

Section: Observability · An open-source framework to evaluate, test and monitor ML and LLM-powered systems.

ActiveScore 75

Awesome Machine Learning

Section: Python · Interactive reports to analyze machine learning models during validation or production monitoring.

FreshScore 93

Awesome MLOps

Section: Visual Analysis and Debugging · Interactive reports to analyze ML models during validation or production monitoring.

FreshScore 80

Awesome Open Source AI

Section: 8. MLOps / LLMOps & Production · ML & LLM monitoring framework.

FreshScore 89

Awesome Production Machine Learning

Section: Evaluation and Monitoring · Evidently is an open-source framework to evaluate, test and monitor ML and LLM-powered systems.

FreshScore 92

Awesome Python Data Science

Section: Data Validation · Evaluate and monitor ML models from validation to production.

ActiveScore 71

LiteLLM

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…

In 16 listsDetails

Opik

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…

In 16 listsDetails

Colossal-AI

(from Hpcaitech) - A Unified Deep Learning System for Large-Scale Parallel Training (1D, 2D, 2.5D, 3D and sequence parallelism, and ZeRO protocol).

In 14 listsDetails

Haystack

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,…

In 13 listsDetails

vLLM

State-of-the-art serving engine with PagedAttention and continuous batching. Currently the fastest production-grade LLM server.

In 11 listsDetails

MindsDB

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.

In 11 listsDetails

Langfuse

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

In 10 listsDetails

Deepchecks

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.

In 8 listsDetails