Function Application via REST

All MLDB user-defined Functions (including machine learning models created by Procedures) are not only available within SQL queries also automatically accessible as REST Endpoints, which can be used to apply machine learning models in a streaming/real-time process. Functions support input values passed in as query string parameters or as a JSON payload. In both cases, the REST call must be a GET. For example, functions can be applied with query string parameters via a REST API call like GET /v1/functions/<id>/application?input={<values>}.

Example

Let's look at a hypothetical Function with id example whose type defined the following input and output values:

• Input
  • x: integer
  • y: row of integers
• Output:
  • scaled_y: row of integers, each of which is the corresponding value of y times x
  • sum_scaled_y: integer, the sum of the values of scaled_y

We could apply this Function via REST like so:

mldb.get("/v1/functions/example/application", input={"x":2,"y":{"a":3,"b":4}})

And we would receive the following output:

{
    "output": {
        "sum_scaled_y": 14,
        "scaled_y": [
            [ "a", [ 6, "-Inf" ] ], 
            [ "b", [ 8, "-Inf" ] ]
        ]
    }
}

The same output can be obtained by providing the arguments using a JSON payload:

mldb.get("/v1/functions/example/application", data={"input": {"x":2,"y":{"a":3,"b":4}}})

See also

• Procedures and Functions Tutorial