Record latency evaluation metric

Scope

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The record latency metric evaluates generative AI assets and machine learning models.

• Generative AI tasks:
  • Text summarization
  • Text classification
  • Content generation
  • Entity extraction
  • Question answering
  • Retrieval Augmented Generation (RAG)
• Machine learning problem type:
  • Binary classification
  • Multiclass classification
  • Regression
• Supported languages: English

Evaluation process

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The average, maximum, median, and minimum record latency for scoring requests and transaction records are calculated during model health monitor evaluations.

To calculate the record latency metric, response_time value from your scoring requests is used to track the time that your model deployment takes to process scoring requests.

For watsonx.ai Runtime deployments, the response_time value is automatically detected when you configure evaluations.

For external and custom deployments, you must specify the response_time value when you send scoring requests to calculate throughput and latency as shown in the following example from the Python SDK:

    from ibm_watson_openscale.supporting_classes.payload_record import PayloadRecord            
        client.data_sets.store_records(
        data_set_id=payload_data_set_id, 
        request_body=[
        PayloadRecord(
            scoring_id=<uuid>,
            request=openscale_input,
            response=openscale_output,
            response_time=<response_time>,  
            user_id=<user_id>)
                    ]
        ) 
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Parent topic: Evaluation metrics