Common Challenges
High Dimensional Metrics
Calculating rank-aware evaluation metrics to fully capture recommendation performance based on relevancy, rank order, and business impact
Dynamic Production Data
Continually evolving production data that degrades recommendations and negatively impacts customer retention and churn
Large Scale Predictions
Issues that are difficult to identify due to thousands of features that could impact rank and relevancy
Purpose-Built Support
Use Arize to address common (and uncommon) challenges associated with your recommendation system.
Ship More Relevant Recommendations at Scale
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Evaluate recommendation performance based on relevancy and rank order with natively calculated rank-aware metrics
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Proactively monitor rank-aware metrics to mitigate the risk of inaccurate or suboptimal recommendations over time
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Tie model metrics back to business impact with custom metrics informed by real-time model performance
Prevent Bias, Data Quality, and Feature Engineering Issues
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Calculate case-specific recommendation problems such as diversity, novelty, or popularity to surface non-obvious model issues
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Dynamically visualize model data with the ability to filter by features, compare production versions, and evaluate how different datasets impact rank group performance
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Full visibility into how specific features impact model performance and model drift
Troubleshoot Rank Performance In Minutes
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Automatically surface problematic features, groups, and ranks dragging down model performance with dedicated performance troubleshooting workflows
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Drill into features impacting your recommendations with performance insights that provide a view of areas within a feature to improve
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Gain a single, sharable view of model performance with key rank-aware metrics, business impact, and recommendation case-specific metrics with purpose-built dashboards
Industry Examples
