Evidently
Open-Source-Framework für ML- und LLM-Observability. Evaluieren, testen und überwachen Sie jedes KI-gestützte System oder jede Daten-Pipeline mit über 100 Metriken.
Überblick
Anforderungen
Empfohlener VPS
Hostinger · KVM 4
4 vCPU · 16384 MB · 200 GB
Hostinger · KVM 4
4 vCPU · 16384 MB · 200 GB
Hostinger · KVM 8
8 vCPU · 32256 MB · 400 GB
Affiliate-Hinweis
Docker Compose
# Generated by Run This Ai — docker-compose.yml
services:
evidently:
image: evidently/evidently-service:latest
restart: unless-stopped
ports:
- 8080:8080
volumes:
- ./data/evidently:/data
Verwandte Tools
NextChat
Cross-platform ChatGPT web UI with multi-model support for Ollama, Claude, Gemini, and more
Lobe Chat
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Open WebUI
User-friendly WebUI for LLMs (Ollama, OpenAI API)
text-generation-webui
Run local LLMs with a powerful web interface — text, vision, tool-calling, and OpenAI-compatible API
Streamlit
Build and share data apps in pure Python — fast
Gradio
Build and share delightful machine learning apps in Python
Anleitungen & Artikel
Evidently Tutorial: From pip to Production Monitoring in 15 Minutes
Step-by-step tutorial for Evidently — install via pip, run LLM evaluations with descriptors, detect data drift in tabular data, and deploy the self-hosted Monitoring UI with Docker.
Evidently Guide: Open-Source ML and LLM Observability Framework
A comprehensive guide to Evidently — the open-source Python framework for evaluating, testing, and monitoring ML and LLM systems. Covers 100+ metrics, Reports vs Test Suites, LLM evals with descriptors, and self-hosted monitoring.