Verta Model Monitoring
Real-Time Model Observability
Get real-time insights and alerts on model performance and data characteristics, debug anomalies, and initiate proactive actions.
Fully configurable monitoring for any serving infrastructure.
Know when models are failing
Track input, output, and intermediate results
Monitor data distribution and complex statistical summaries - data quality, null values, and missing data
Detect data drift and outliers in input features and predictions
Ingest ground truth and monitor quality metrics like accuracy, precision, recall, F1 score, etc.

Quickly find the root cause
Advanced query and filter supporting millions of summaries and perform cohort analysis
Root cause analysis and outlier detection by running correlations across data samples, time ranges, and metadata tags
Connect pre-production (model catalog and experiment tracking) and production systems for end to end visibility

Close the loop by fast recovery
Receive actionable alerts for performance degradation, or drift
Track model releases, identify unexpected behavior, and automate production rollback
Automate remedial action like fallback, model retrain, human in the loop
Integrate into DevOps and alerting tools like Slack, Pagerduty, and more

Automated monitoring
No setup required to monitor endpoints running on Verta
Model predictions, features, alerts and thresholds are automatically defined by the system with the option to customize
Monitor custom metrics and build your own charts and visualizations (e.g. confusion matrix, PR curve, ROC curve and more)
Fully customizable dashboards and interactive charts

Take a Tour
One platform, all of your model delivery needs.
Manage
Full-lifecycle model management from experiment tracking to production registry
Deploy
Ensure production-quality operations with reliable governance and auditing.
Operate
Reliable batch and real-time inference & serving on any k8s infrastructure.
Monitor
Keep models relevant with real-time decay monitoring and logging.
Compatibility
We Integrate With Your AI-ML Stack
Verta supports all of these popular platforms and frameworks—plus many, many more.












Don't take our word for it. See what others are saying.
Scribd utilizes machine learning to optimize search, make recommendations, and improve new features.
LeadCrunch's Data Science teams create Machine Learning models that help B2B companies find better prospects faster.
A leading collaboration platform utilizes ML to prevent abuse, make recommendations, and improve user experience.
With Verta, we can log, analyze, and get alerted on changes to the data distribution of our classification model and pipeline. Additionally, we have better visibility on how our intermediate data changes over time. Throwing data on an s3 bucket and waiting for a slow day to look at it means it’s never going to get attention. But spending 30 extra seconds to log it as part of a data pipeline is well worth it.”
Jenn Flynn, Principal Data Scientist at LeadCrunch