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On this page
  • Additional Context
  • Why Custom Metrics Are Important
  • Custom Metric Use Cases
  • Custom Metrics How-to Guide
  • Frequently Asked Questions
  • Related Resources

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  1. Glossary
  2. Product Concepts

Custom Metric

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Last updated 20 days ago

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are user-defined monitoring measures created using (FQL) within the AI/ML/GenAI observability platform. They allow data scientists and ML engineers to extend beyond built-in metrics by defining their own calculations and thresholds for monitoring model performance.

Additional Context

Custom Metrics transform standard observability into a tailored monitoring solution by enabling teams to implement domain-specific KPIs that complement built-in metrics like data drift and data integrity. This flexibility allows organizations to focus on metrics that directly impact their business objectives rather than solely relying on standard technical indicators.

Why Custom Metrics Are Important

The roles of Custom Metrics in machine learning and model monitoring include:

  • Addressing unique business requirements not covered by standard metrics

  • Creating composite metrics that combine multiple signals into actionable insights

  • Implementing domain-specific calculations that reflect business KPIs

  • Enabling proactive alerting on custom-defined thresholds

Custom Metric Use Cases

  • Business-focused metrics: Metrics that directly tie to business outcomes like conversion rates, revenue impact, or customer satisfaction

  • Composite technical metrics: Combined measures that blend multiple data signals for more holistic monitoring

  • Data quality extensions: Custom definitions of what constitutes data quality in specific domains

Custom Metrics How-to Guide

  1. Identify the metric need

    • Determine what performance aspects aren't covered by built-in metrics

  2. Design the FQL formula

    • Write the formula using Fiddler Query Language syntax using the UI or API

  3. Test on historical data

    • Validate that your metric catches issues using past data

  4. Iterate based on results

    • Refine the metric definition as you learn from real-world monitoring

Frequently Asked Questions

Q: How do Custom Metrics differ from built-in metrics?

Custom Metrics allow you to define domain-specific calculations using FQL that may not be available through pre-built metrics, giving you flexibility to monitor aspects of your AI systems most relevant to your business.

Q: Can Custom Metrics be used for alerting?

Yes, Custom Metrics integrate seamlessly with the platform's alerting system, allowing you to set thresholds and receive notifications when your user-defined metrics exceed acceptable ranges.

Q: What technical knowledge is required to create Custom Metrics?

Basic understanding of SQL-like query languages and knowledge of your data schema are sufficient for creating most Custom Metrics with FQL.

Related Resources

Custom Metrics
Fiddler Query Language
Custom Metrics Product Guide
Fiddler Query Language
Custom Metrics REST API Guide