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.
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.
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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.
See documentation
Get Started with AI Observability
Book a personalized 1:1 demo with our team or sign up for a free account.
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