Hit the ground running with 100+ pre-built metrics covering data, drift, performance, bias, and explainability. All metrics are accessible, customizable, and consumable from the metric store, SDK, and API.
Your ML. Your rules. Use Superwise’s dynamic anomaly detection engine that takes seasonality and temporality into account. Tune the monitor’s sensitivity and detection direction, or even configure a fixed threshold.
The real deal: model evaluation vs. model monitoring
Model evaluation and model monitoring are not the same thing. They may sound similar, but they are fundamentally different. Let’s see how.
Show me the ML monitoring policy!
Model observability may begin with metric visibility, but it’s easy to get lost in a sea of metrics and dashboards without proactive monitoring to detect issues. But with so much variability in ML use cases where each may require different metrics to track, it’s challenging to get started with actionable ML monitoring. If you can’t…
Measuring the performance of sub-groups
Just like in some cases “the whole is greater than the sum of its parts”, in machine learning “the performance of the model is not a reflection of the sum of its sub-groups.” Indeed, the good performance of machine learning models does not necessarily mean that every sub-group is optimized. Quite the contrary. Models don’t…