New! Use Evidently to evaluate LLM-powered products

Collaborative ML observability

Ensure reliable ML performance in production. Get real-time visibility, detect issues and fix them fast.
Evidently AI Reports
Evaluate

Know your models

Understand the data and models before they go live. Generate model cards and performance reports with one command.
Evidently AI Test suites
Test

Ship with confidence

Run structured checks at data ingestion, model scoring, or CI/CD. Catch wrong inputs, unseen values, or quality dips before users do.
Evidently AI Monitoring dashboard
Monitor

Get live insights

Track data and model health for all production ML systems. Identify drifts and unexpected behavior. Get alerts to intervene or retrain.
Evidently AI ML debugging
Debug

Speed up root cause analysis

Dig into specific periods and features with pre-built summaries and plots. Diagnose issues and find areas to improve. 
Collaborative AI Observability platform
Collaborate

Share your findings

Create custom views for all stakeholders. Communicate the value the models bring and how well they work to boost trust in ML. 
Workflow

Control production ML quality end-to-end

Track every step, from incoming data to model predictions.
Data quality
Keep tabs on production model features. Detect if they go stale or deviate from typical patterns.
Data drift
Spot data distribution shifts. Know about changes in the model environment and unexpected outputs.
Model quality
Check the true model quality once you get the labels. Find low-performing segments and interpret the errors.
Metrics

100+ built-in evaluations

Kickstart your analysis with a library of metrics. Add custom checks when you need them.
Data statistics
Capture and visualize data summaries over time.
Distribution shifts
Assess data drift with 20+ statistical tests and distance metrics.
Classification
Evaluate quality from accuracy to classification bias.
Ranking
Measure ranking performance with NDCG, MAP, and more.
Feature ranges
Know if values are out of expected bounds.
Missing values
Detect feature outages or empty rows.
Regression
See if your model under- or over-predicts.
Recommender systems
Track novelty, diversity, or serendipity of recommendations.
New categories
Identify and handle previously unseen categories.
Correlations
Observe feature relationships and how they change.
Embeddings drift
Analyze shifts in vector representations.
Text descriptors
Track text properties, from length to sentiment. 
See documentation

Get Started with AI Observability

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