Model predictions

Question

Hi,
I build a Bagged GLM model on azure studio with tons of data split 50/50 as training and testing input. Mow I am trying to deploy it as web service, it wont let me to input from the start point.

1) Where should I input a simple data to the model? Before the loop for bagging when I got a single request? Would the result same for fast requests/input?
beggin<-function(trainx,testx,length_divisor=4,iterations=100)
prediction<-foreach(m=1:iterations, .combine=rbind) %dopar% {
predict(glm,newdata=testx, na.action=na.omit)
}
data.set<-rowMeans(prediction)
2) Another model built using use r-script modules. Each time it predicts almost the same value using our algorithm not from the studio, the variation of the result was trivial to the .0xx

Thanks
N.A.W.

Answer 1

Hello,

After you creating your web service, you need to call it as below.

I am giving Python as an example: https://learn.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service?tabs=python#call-the-service-python

import requests  
import json  
  
# URL for the web service  
scoring_uri = '<your web service URI>'  
# If the service is authenticated, set the key or token  
key = '<your key or token>'  
  
# Two sets of data to score, so we get two results back  
data = {"data":  
        [  
            [  
                0.0199132141783263,  
                0.0506801187398187,  
                0.104808689473925,  
                0.0700725447072635,  
                -0.0359677812752396,  
                -0.0266789028311707,  
                -0.0249926566315915,  
                -0.00259226199818282,  
                0.00371173823343597,  
                0.0403433716478807  
            ],  
            [  
                -0.0127796318808497,  
                -0.044641636506989,  
                0.0606183944448076,  
                0.0528581912385822,  
                0.0479653430750293,  
                0.0293746718291555,  
                -0.0176293810234174,  
                0.0343088588777263,  
                0.0702112981933102,  
                0.00720651632920303]  
        ]  
        }  
# Convert to JSON string  
input_data = json.dumps(data)  
  
# Set the content type  
headers = {'Content-Type': 'application/json'}  
# If authentication is enabled, set the authorization header  
headers['Authorization'] = f'Bearer {key}'  
  
# Make the request and display the response  
resp = requests.post(scoring_uri, input_data, headers=headers)  
print(resp.text)  

More information please refer to the documentation. Please let me know if you need more details.

Regards,
Yutong