How To Fix FINS_GRIR_ML_MESSAGE405 - Cannot train ML model as historical data is empty

SAP Error Message - Details

• Message type: E = Error

• Message class: FINS_GRIR_ML_MESSAGE -

• Message number: 405

• Message text: Cannot train ML model as historical data is empty

Self-Explanatory Message

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Smart SAP Assistant

• What is the cause and solution for SAP error message FINS_GRIR_ML_MESSAGE405 - Cannot train ML model as historical data is empty ?

  The SAP error message FINS_GRIR_ML_MESSAGE405 indicates that the system is unable to train a machine learning (ML) model because there is no historical data available. This error typically arises in the context of machine learning applications within SAP, particularly when trying to implement predictive analytics or similar functionalities.
  Cause:
  
  
  Lack of Historical Data: The primary cause of this error is that the dataset required for training the ML model is empty. This could happen if:
  
  No transactions have been recorded in the relevant period.
  Data has not been properly loaded or extracted from the source systems.
  Filters applied to the data extraction process are too restrictive, resulting in no data being available.
  
  
  
  Configuration Issues: There may be issues with the configuration of the data sources or the ML model itself, leading to an inability to access the necessary historical data.
  
  
  Data Retention Policies: If data retention policies are in place, older data may have been archived or deleted, leaving insufficient data for model training.
  
  
  Solution:
  
  
  Check Data Availability:
  
  Verify that there is historical data available in the relevant tables or datasets. You can do this by running queries or reports to check for the presence of data.
  Ensure that the data is within the expected time frame for the model training.
  
  
  
  Data Extraction:
  
  If data is missing, ensure that the data extraction process is correctly configured to pull the necessary historical data.
  Review any filters or parameters that may be limiting the data being extracted.
  
  
  
  Configuration Review:
  
  Check the configuration settings for the ML model to ensure that they are correctly set up to point to the right data sources.
  Ensure that the model is configured to use the correct data fields and that there are no discrepancies in the data schema.
  
  
  
  Data Retention Settings:
  
  Review the data retention policies to ensure that they are not overly restrictive. If necessary, adjust these policies to retain more historical data.
  
  
  
  Testing:
  
  After making the necessary adjustments, attempt to retrain the ML model to see if the issue is resolved.
  
  
  
  Related Information:
  
  SAP Documentation: Refer to the official SAP documentation for machine learning and data management to understand the requirements for training models and the expected data formats.
  SAP Community: Engage with the SAP Community forums to see if other users have encountered similar issues and what solutions they have found.
  Support: If the issue persists after checking the above points, consider reaching out to SAP support for further assistance.
  
  By following these steps, you should be able to identify the root cause of the error and implement a solution to enable the training of the ML model.