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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 

Expand
titleAPI to infer from a model using provided dataset

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

HTTP RequestValue
Method

POST

Header

Authorization: Bearer {token}

URL

{prefix}/models/

Query Parameters

{

  "dbToken": "string";
  "modelID":"string"

  "data":{
      "sourceType":"string",
      "endpoint":"string",
      "bearerToken":"string",
      "query":"string"
  }
}

Example

{

  "dbToken": "D41C4A382C27A4B5DF824E2D4F148";
  "modelID":"6d4b571a-80ca-41ef-bc67-b158f4352ad8"
  "data":{
      "sourceType":"snowflake",
      "endpoint":"some/endpoint",
      "bearerToken":"7CA4D3C152646DDEFB527A958C45B",
      "query":"SELECT foo FROM bar WHERE baz"
  }
}




Error - Apply to all API calls above

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