We’re excited to release into beta v1.0 of Elemeta, our open-source library for exploring, monitoring, and extracting features from unstructured data.
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.
Understand and explain any decision
Explainability metrics
Explain and diagnose model behavior using prediction-level feature attribution, cohort, what-if, and counterfactual analysis.
See what Superwise can do for you
Try Superwise out or contact us to learn more.
Featured resources
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.Â
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.
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