Collaborative ML observability
Ensure reliable ML performance in production. Get real-time visibility, detect issues and fix them fast.
Evaluate
Know your models
Understand the data and models before they go live. Generate model cards and performance reports with one command.
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.
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.
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.Â
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.Â
WORKFLOW
Control production ML quality end-to-end
Evaluate input and output quality for predictive tasks, including classification, regression, ranking and recommendations.
Data drift
No model lasts forever. Detect shifts in model inputs and outputs to get ahead of issues.
Get early warnings on model decay without labeled data.
Understand changes in the environment and feature distributions over time.
Monitor for changes in text, tabular data and embeddings.
Data quality
Great models run on great data. Stay on top of data quality across the ML lifecycle.
Automatically profile and visualize your datasets.
Spot nulls, duplicates, unexpected values and range violations in production pipelines.
Inspect and fix issues before they impact the model performance and downstream process.
Model performance
Track model quality for classification, regression, ranking, recommender systems and more.
Get out-of-the-box performance overview with rich visuals. Grasp trends and catch deviations easily.
Ensure the models comply with your expectations when you deploy, retrain and update them.
Find the root cause of quality drops. Go beyond aggregates to see why model fails.
Collaboration
Built for teams
Bring engineers, product managers, and domain experts to collaborate on AI quality.
UI or API? You choose. You can run all checks programmatically or using the web interface.
Easily share evaluation results to communicate progress and show examples.
Get started
Metrics
100+ built-in evaluations
Kickstart your analysis with a library of metrics. Add custom checks when you need them.
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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.Â
See documentation
Start testing your AI systems today
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