Custom Metrics

Overview

Custom metrics offer the capability to define metrics that align precisely with your machine learning requirements. Whether it's tracking business KPIs, crafting specialized performance assessments, or computing weighted averages, custom metrics empower you to tailor measurements to your specific needs. Seamlessly integrate these custom metrics throughout Fiddler, leveraging them in dashboards, alerting, and performance tracking.

Create user-defined metrics by employing a simple query language we call Fiddler Query Language (FQL). FQL enables you to leverage your model's features, metadata, predictions, and outcomes for new data fields using a rich array of aggregations, operators, and metric functions, thereby expanding the depth of your analytical insights.

How to Define a Custom Metric

Build custom metrics effortlessly with Fiddler's intuitive Excel-formula-like syntax. Once a custom metric is defined, Fiddler distinguishes itself by seamlessly managing time granularity and ranges within the charting, dashboarding, and analytics experience. This empowers you to effortlessly adjust time range and granularity without the need to modify your query, ensuring a smooth and efficient analytical experience.

Adding a Custom Metric

From the model schema page, you can access the model's custom metrics by clicking the Custom Metrics tab at the top of the page. Then click Add Custom Metric to add a new Custom Metric. Finally, enter the name, description, and FQL definition for your custom metric and click Save.

Accessing Custom Metrics in Charts and Alerts

After your custom metric is saved, you can use it in your chart and alert definitions.

Charts

Set Metric Type to Custom Metric and select your desired custom metric.

Alerts

When creating a new alert rule, set Metric Type to Custom Metric, and under the Metric field select your desired custom metric or author a new metric to use.

Modifying Custom Metrics

Since alerts can be set on Custom Metrics, making modifications to a metric may introduce inconsistencies in alerts.

🚧 Therefore, custom metrics cannot be modified once they are created.

If you'd like to try out a new metric, you can create a new one with a different name and definition.

Deleting Custom Metrics

To delete a custom metric using the Python client, see CustomMetric.delete(). Alternatively, from the custom metrics tab, you can delete a metric by clicking the trash icon next to the metric record.

Examples

📘 Custom metrics must return either:

• an aggregate (produced by aggregate functions or built-in metric functions)

• a combination of aggregates

Simple Metric

Given this example use case:

If an event is a false negative, assign a value of -40. If the event is a false positive, assign a value of -400. If the event is a true positive or true negative, then assign a value of 250.

Create a new Custom Metric with the following FQL formula:

average(if(fn(), -40, if(fp(), -400, 250)))

Fiddler offers many convenience functions such as fp() and fn(). Alternatively, we could also identify false positives and false negatives the old fashioned way.

average(if(Prediction < 0.5 and Target == 1, -40, if(Prediction >= 0.5 and Target == 0, -400, 250)))

Here, we assume Prediction is the name of the output column for a binary classifier and Target is the name of our label column.

Tweedie Loss

In our next example, we provide an example implementation of the Tweedie Loss Function. Here, Target is the name of the target column and Prediction is the name of the prediction/output column.

average((Target \* Prediction ^ (1 - 0.5)) / (1 - 0.5) + Prediction ^ (2 - 0.5) / (2 - 0.5))

Quantile/Percentile Metrics

Quantile metrics are essential for understanding the distribution of your data and tracking percentile-based performance indicators. Unlike averages, which can be skewed by outliers, quantiles provide robust insights into the actual behavior of your model across different percentiles.

Use Case: Monitoring ML Model Latency

Track the median (50th percentile) inference time to understand typical model performance:

quantile(inference_time_ms, level=0.5)

Use Case: SLA Compliance Tracking

Monitor 95th percentile latency to ensure most requests meet performance SLAs:

quantile(response_time_ms, level=0.95)

Use Case: Outlier Detection

Track the 99th percentile of prediction scores to identify extreme predictions:

quantile(prediction_score, level=0.99)

Use Case: Statistical Distribution Analysis

Compute quartiles to understand the distribution of a feature or prediction:

quantile(feature_value, level=0.25)  # First quartile (Q1)
quantile(feature_value, level=0.5)   # Median (Q2)
quantile(feature_value, level=0.75)  # Third quartile (Q3)

📘 Why use quantiles instead of averages?

Quantiles provide a more complete picture of your data's distribution. While averages can be heavily influenced by outliers, percentiles show you the actual values at specific points in your distribution. For example, a 95th percentile latency of 500ms means 95% of your requests complete in 500ms or less, regardless of how slow the remaining 5% might be.

Modifying Custom Metrics

Since alerts can be set on Custom Metrics, modifying a metric may introduce inconsistencies in those alerts.

🚧 Therefore, custom metrics cannot be modified once they are created.

If you'd like to try out a new metric, you can create a new one with a different name and definition.