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4. User interacts with the result BI presented and decides what can be a good model to make a prediction on certain set of data. Please note that the model can also be returned as the result of the training step described in the previous section. In the case, the user may bypass these 2 steps and go directly to see the result.
5. This step is similar to Once the user decides which model to run for prediction. BI will compile all required data and call OBAIC.
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Pass by value is recommended only for small data set Model configuration is based on configs from the open-source Ludwig project. At a minimum, we should be able to define inputs and outputs in a fairly standard way. Other model configuration parameters are subsumed by the options field. The data stanza provides a bearer token allowing the ML provider to access the required data table(s) for training. The provided SQL query indicates how the training data should be extracted from the source. Don't be confused with the Bearer token which is used to authenticate with OBAIC, and the dbToken which is created in 2(a) and AI platform will use that to access the data source for training
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