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How To Fix ML_CAI_MESSAGE054 - Training model &1 is not ready to be activated.


ML_CAI_MESSAGE054 - Overview

  • Message type: E = Error

  • Message class: ML_CAI_MESSAGE -

  • Message number: 054

  • Message text: Training model &1 is not ready to be activated.

  • Show details Hide details


  • Self-Explanatory Message

    Since SAP believes that this specific error message is 'self-explanatory,' no more information has been given.The majority of messages in the SAP system have a message text, however this is frequently insufficient to comprehend or resolve the problem.

    To make things easier, more detailed information is frequently added to describe the issue, how to fix it, and the necessary steps or configuration modifications.

    Unfortunately, there isn't any extra information in this error notice.



    What else can you do?

    First, use our AnswerBot below to get a possible cause and solution (requires a premium subscription).

    Also, review the in-depth Common Questions & Answers listed below; you could discover a solution there or be able to connect with others who have faced similar challenges.

    You can also try searching the SAP support portal (support.sap.com) but you need a special user ID to access it. It is possible that an SAP support note exists that provides additional details about the mistake or even steps for fixing it.


ML_CAI_MESSAGE054 - Details

  • The SAP error message ML_CAI_MESSAGE054 indicates that a training model in the context of SAP's Machine Learning capabilities is not ready to be activated. This can occur for several reasons, and understanding the cause is essential for resolving the issue.
    Possible Causes:
    
    
    Incomplete Training: The model may not have completed its training process successfully. This could be due to insufficient data, errors during the training phase, or other interruptions.
    
    
    Validation Issues: The model might not have passed the necessary validation checks. This could include performance metrics not meeting the required thresholds.
    
    
    Configuration Errors: There may be configuration issues in the model setup, such as incorrect parameters or missing dependencies.
    
    
    Data Quality Issues: The training data may have quality issues, such as missing values, incorrect formats, or inconsistencies that prevent the model from being activated.
    
    
    Versioning Problems: If there are multiple versions of the model, the one you are trying to activate may not be the latest

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