Serving several ML models and formula usage inside

Run serving service

To start serving the trained ML model, you need the files with the saved ML model and transformations.

The following CLI command will create the API server with the saved model:

insolver_serving -service sflask -configfile settings.py -models_folder several_models/models/ -transforms_folder several_models/transforms/

You can choose the server based on Flask or FastApi with the option service:

• -service sflask

• -service sfastapi

The default endpoint is http://localhost:8000/predict, but you can change it with the parameters -ip and -port.

For example:

insolver_serving -service sflask -ip 127.0.0.1 -port 5040 -configfile settings.py -models_folder several_models/models/ -transforms_folder several_models/transforms/ -transforms_module path_to_transforms_module

Using serving service

After starting the ML serving service, you can use it via REST API.

Example of the request to http://127.0.0.10:5040/predict endpoint:

{"df": 
  {"Exposure":{"145813":0.617},
    "LicAge":{"145813":602},
    "RecordBeg":{"145813":"2004-05-19"},
    "RecordEnd":{"145813":"2009-05-19"},
    "Gender":{"145813":"Male"},
    "MariStat":{"145813":"Other"},
    "SocioCateg":{"145813":"CSP60"},
    "VehUsage":{"145813":"Private"},
    "DrivAge":{"145813":68},
    "HasKmLimit":{"145813":0},
    "BonusMalus":{"145813":50},
    "ClaimAmount":{"145813":5377.204531722},
    "ClaimInd":{"145813":1},
    "Dataset":{"145813":5},
    "ClaimNbResp":{"145813":1.0},
    "ClaimNbNonResp":{"145813":0.0},
    "ClaimNbParking":{"145813":1.0},
    "ClaimNbFireTheft":{"145813":0.0},
    "ClaimNbWindscreen":{"145813":1.0},
    "OutUseNb":{"145813":0.0},
    "RiskArea":{"145813":4.0}}
}

LightGBM Models to this example can be found here.