WHAT IS MODEL PERFORMANCE MONITORING?
How ML models stay relevant and work as intended.
ACCURATE ML MODELS
Tackle model-performance degradation after deployment.
- Resolve data drift and concept drift to maintain relevant predictions.
- Configure alerts from New Relic alerts and applied intelligence to resolve problems and reduce noise.
- View statistical data of model performance and model features to improve results.
Bring ML telemetry. Reach informed decisions.
- Bring your own data (features, prediction values, etc.) as inference data, aggregated statistics, or custom metrics.
- Use integrations (for example, AWS SageMaker) to add ML model telemetry from other platforms.
- Leverage our diverse community of partners for open-platform data ingestion.
Visualize models in moments. See their value rise.
- Find and add charts that are tailored for specific use cases.
- Get instant visibility into your models with out-of-the-box performance-monitoring dashboards.
- Easily track model predictions and drift over time for insights at a glance.
ALIGNMENT ACROSS TEAMS
Solve collaboration around ML with data.
- Share one source of data to shrink inefficient gaps between ML engineer, DevOps, and data-science teams.
- See performance in context on the only full-stack observability platform that tracks ML models.
- Collaborate in a production environment and respond to alerts before your business is impacted.