Monitoring

Detect data drift

Ensure data quality

Monitor availability + latency

CPU Utilization

max

84.0%

avg

52.3%

Feature values

Easily differentiate between organic shifts, unexpected format changes in upstream data sources, and development mistakes. Get alerted automatically before issues cause your model performance to suffer.

Pipeline execution

When feature engineering pipelines break, you need visibility into why. Aggregate logs and metrics by query, cron job, migration, and even individual resolvers. Every line of code you write is automatically instrumented so you can easily diagnose issues.

Data freshness

When ETL pipelines break or third-party data vendors have outages, models can wind up with stale inputs that lead to inaccurate predictions. Monitor freshness across batch, streaming, and realtime data sources to make sure that your models execute with up-to-date data.

Data lineage

Upstream data quality issues can cause your features to suffer. Automatically track data provenance for derived features, so that you can understand which upstream data sources cause problems and escalate issues to the relevant data owners or external vendors.

Incident response

Production-grade machine learning requires production-grade alerting. Integrate with the alerting systems you already use like Pagerduty, or chat systems like Slack, to keep your team informed about issues. Configure alerting thresholds so that you get notified when pipeline behavior doesn’t match expectations.