Hello Shikha,
Sorry for the delayed response, as it has been a busy morning 🙂
I am pleased you managed to resolve the issue using the instructions in the last line of the KB article.
Indeed, you have to repeat the training until it reaches the correct version and after each NLU Model training, delete the [ml_solution] record that it created with the older version number. I will update this KB article, and try to make these steps a little bit clearer to fix the issue.
This situation occurs when you update an existing NLU Model on the target instance from a source instance where the NLU Model has been trained more often, so that the NLU Model versions are out of sync.
I will also provide feedback to the Dev team to see how we can prevent this situation from occurring in the future when transferring NLU Models between instances. We have made further improvements with NLU Model transfers between instances in San Diego, but I have not checked to see if this issue persists with NLU Model versioning.
Best regards,
Brian
