Predict
Predictions can be made by calling the HTTP endpoint associated with the model
deployment. The ModelDeployment object url attribute
specifies the endpoint. You could also use the
ModelDeployment object with the .predict() method. The format of
the data that is passed to the HTTP endpoint depends on the setup of
the model artifact. The default setup is to pass in a Python dictionary
that has been converted to a JSON data structure. The first level
defines the feature names. The second level uses an identifier for the
observation (for example, row in the dataframe), and the value associated with
it. Assuming the model has features F1, F2, F3, F4, and F5, then
the observations are identified by the values 0, 1, and 2 and the data would look like this:
Index |
F1 |
F2 |
F3 |
F4 |
F5 |
|---|---|---|---|---|---|
0 |
11 |
12 |
13 |
14 |
15 |
1 |
21 |
22 |
23 |
24 |
25 |
2 |
31 |
32 |
33 |
34 |
35 |
The Python dictionary representation would be:
test = {
'F1': { 0: 11, 1: 21, 2: 31},
'F2': { 0: 12, 1: 22, 2: 32},
'F3': { 0: 13, 1: 23, 2: 33},
'F4': { 0: 14, 1: 24, 2: 34},
'F5': { 0: 15, 1: 25, 2: 35}
}
You can use the ModelDeployment object to call the HTTP endpoint. The returned
result is the predictions for the three observations.
deployment.predict(test)
{'prediction': [0, 2, 0]}