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Key performance indicator

As explained in Machine Learning System Design

A key performance indicator that quantitatively measures progress toward an organization's business objective.

A key performance indicator (KPI) measures progress toward a business goal. For a production ML system, business KPIs answer a question that model-quality metrics alone cannot: is the system delivering value? Revenue or conversion rate may be useful KPIs when a model supports user acquisition. section 14.4.4

When monitoring an ML system,

  • Track both absolute and relative KPI values to get a fuller view of its effect. section 14.4.4
  • Choose KPIs with data scientists and business stakeholders so monitoring reflects business needs. section 14.4.4
  • Use a proxy or interpretable check when a KPI is difficult to measure or the system’s effect is hard to isolate. section 14.4.4

A falling KPI is a reason to investigate, not proof that the model caused the decline. Check the model, data, and software for the cause. Business KPIs sit above those layers in monitoring because they show whether the whole system meets its goals, while lower-level checks help explain what went wrong. section 14.4.4 section 14.6

How Machine Learning System Design covers it

How deep it goes
Gives practical monitoring advice and measurement cautions, but does not specify KPI formulas or alert thresholds.
Examples
Uses revenue and conversion rates as possible KPIs for a model supporting user acquisition.
What you can do after
Evaluate/understand whether an ML system delivers business value, while investigating other causes when a KPI changes.
How it connects
Connects business KPIs to model quality, data quality, software health, and model decay in production monitoring.

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This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.