Problem solved

Evidently helps teams detect and diagnose issues in ML and LLM systems by providing automated evaluation, testing, and monitoring capabilities, reducing the risk of model drift, data quality problems, and poor generative outputs in production.

Use cases

• Monitor production ML models for data and concept drift
• Evaluate LLM responses for quality, relevance, and safety
• Generate automated test suites for ML pipeline validation
• Track model performance metrics over time
• Create interactive reports for model validation and debugging
• Integrate observability checks into CI/CD workflows

Audience

• ML Engineers
• Data Scientists
• Developers
• Researchers

Pros

• 100+ ready-to-use metrics for ML and LLM
• Open source with no vendor lock-in
• Supports both tabular data and generative AI
• Generates interactive visual reports
• Integrates with Python and CI/CD pipelines

Cons

• Requires Python coding skills
• Self-hosted deployment requires infrastructure for continuous monitoring
• Limited real-time alerting compared to commercial platforms
• May have a learning curve for non-technical users
• Large number of metrics may be overwhelming initially