Description
Evidently AI is an open-source platform dedicated to AI evaluation and observability. It offers tools to ensure AI systems are safe, reliable, and ready for production. The platform supports testing and monitoring of large language models (LLMs) and AI applications across various workflows, including retrieval-augmented generation (RAG) systems and multi-agent setups.
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The platform features a comprehensive database of 250 benchmarks and datasets for evaluating LLMs. These benchmarks cover various capabilities such as reasoning, coding, conversational abilities, and safety. Users can filter benchmarks by specific LLM abilities, making it easier to find relevant tests for their AI use cases.
Evidently AI also provides a database of 800 case studies from over 150 companies, showcasing practical applications of ML and LLM systems. These case studies offer insights into real-world implementations, covering aspects like product design, evaluation criteria, and deployment architecture.
In addition to databases, Evidently AI offers tutorials and courses on AI quality, LLM evaluation, and MLOps. These resources include end-to-end code examples and hands-on learning opportunities for data scientists and engineers.
Evidently AI is trusted by AI teams worldwide and has a community of over 3000 ML practitioners and AI engineers. The platform is open-source, allowing users to contribute and collaborate on improving AI evaluation and observability practices.
- CategoryMLOps & Model Deployment
- Pricing ModelOpen Source
- DeploymentCloud
- PlatformsWeb
Evidently AI's Core Features
Open-source AI evaluation framework
250 LLM benchmarks and datasets
800 ML and LLM case studies
AI quality and MLOps tutorials
Free courses on LLM evaluation
Community of 3000+ AI practitioners
Collaborative testing platform
Real-world ML system insights
How to use Evidently AI?
Configure: Set up evaluation criteria
Use: Test LLMs and monitor AI systems
Optimise: Improve AI reliability and safety
Collaborate: Join community discussions
Evidently AI's Use Cases
- LLM evaluation
- AI observability
- ML system design
- AI quality assurance
- MLOps tutorials
