Detect anomalies before they impact operations
Anomaly Detector learns what normal looks like for each of your pipelines, then tells you when a run stops matching it. The problems it catches are the ones that would otherwise show up a week later in a report nobody trusts.
Continuous monitoring & learning
Anomaly Detector operates silently in the background while all the data operations unfold. It continuously trains its machine learning model by using normal behaviours and deviations observed during the execution of data pipelines. This means that as the data ecosystem evolves, Anomaly Detector grows smarter and more attuned to the data's unique patterns.
Real time anomaly detection & alerts
Anomaly Detector watches pipeline executions and flags behaviour that departs from the pattern it has learned. The alert arrives with the data that triggered it attached, so whoever picks it up starts with evidence instead of a notification and a search.
Scales with the data
The detection model does not need rebuilding as volume grows. Ten pipelines behave the same way as a thousand, and the workspace stays legible at either end, which matters because the point at which most monitoring falls over is the point at which you most need it.
Works with the rest of the platform.
Take a model from data preparation to production in one place. AutoML and experiment tracking, one-click deployment as a versioned API, drift monitoring after go-live.
Read moreForecast demand, capacity, or cash with 25+ time-series algorithms. Scheduled runs, accuracy dashboards, and side-by-side backtests, so you can see which model to trust.
Read moreMove data in at any cadence: scheduled batch, on demand, or change data capture that streams every insert and update as it happens.
Read moreDetect anomalies as they occur
See Anomaly Detector learn your pipelines' normal behaviour and alert on the deviations that matter, live, on your stack.