Flexible model metric store

Hit the ground running with 100+ pre-built and fully customizable metrics covering data, drift, performance, bias, and explainability.

Visibility into any data profile

Data metrics

With our extensive data metric catalog, you’ll be able to measure data distribution, integrity, and quantitative metrics from day one. Have a custom data metric in mind? Code in any custom data metric you need.

Easily detect any drift

Drift metrics

Take control over how you measure data and concept drift with Superwise’s customizable drift metrics. You decide what distance functions, features, datasets, and timeframes are needed to measure drift in your models.

Stay on top of business impact

Performance metrics

Stay ahead of performance degradation. Define the relevant performance metrics for your use cases and track them continuously, no matter how short or long your feedback loop is.

Guard against biased decision-making

Bias metrics

Define and measure bias metrics across different protected classes and sub-groups to protect your business from biased ML and comply with responsible AI standards and regulations.

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Understand and explain any decision

Explainability metrics

Explain and diagnose model behavior using prediction-level feature attribution, cohort, what-if, and counterfactual analysis.

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See what Superwise can do for you
Try Superwise out or contact us to learn more.

Featured resources

Blog

A gentle introduction to ML fairness metrics

In this post, we will cover some common fairness metrics, the math behind them and how to match fairness metrics and use cases.

A hands-on introduction to drift metrics

Instead of focusing on theoretical concepts, this post will explore drift through a hands-on experiment of drift calculations and visualizations. The experiment will help you grasp how the different drift metrics quantify and understand the basic properties of these measures.

Data drift detection basics

Drift in machine learning comes in many shapes and sizes. Although concept drift is the most widely discussed, data drift is the most frequent, also known as covariate shift. This post covers the basics of understanding, measuring, and monitoring data drift in ML systems. Data drift occurs when the data your model is running on…