Tabular Data Classification on Income Dataset¶

In this demo we will:

Create a pipeline which can be used to classify tabular data
Create an explainer
Send a request to a prediction pipeline
View the explanation

Create a pipeline¶

1. Click on Create new deployment¶

Create new deployment

2. Name your pipeline¶

You also need to specify the type as Seldon ML Pipeline.

Name your pipeline

3. Provide model URI¶

In this demo we’re using this model URI: gs://seldon-models/scv2/examples/income/classifier

Provide model URI

4. Specify model runtime¶

The example model is a scikit-learn model, so we’ll select scikit-learn as the runtime.

Specify model runtime

5. Name your model¶

Name your model

Create an explainer¶

1. Select your pipeline¶

Select your pipeline

2. Navigate to the explainer section¶

There you’ll find the create button which will allow you to create an explainer for your pipeline. Navigate to explainer

3. Specify your model data type¶

This model is trained to classify tabular data, so we selected Tabular

Specify your model data type

4. Provide explainer URI¶

The explainer model URI for this pipeline is gs://seldon-models/scv2/examples/income/explainer

Provide explainer URI

Make a prediction¶

1. Select your pipeline¶

Select your pipeline

2. Click on Predict¶

Click on predict

3. Provide prediction data¶

For this example please use this payload

{
    "parameters": {"content_type": "pd"}, 
    "inputs": [
        {"name": "Age", "shape": [1, 1], "datatype": "INT64", "data": [47]},
        {"name": "Workclass", "shape": [1, 1], "datatype": "INT64", "data": [4]},
        {"name": "Education", "shape": [1, 1], "datatype": "INT64", "data": [1]},
        {"name": "Marital Status", "shape": [1, 1], "datatype": "INT64", "data": [1]},
        {"name": "Occupation", "shape": [1, 1], "datatype": "INT64", "data": [1]},
        {"name": "Relationship", "shape": [1, 1], "datatype": "INT64", "data": [3]},
        {"name": "Race", "shape": [1, 1], "datatype": "INT64", "data": [4]},
        {"name": "Sex", "shape": [1, 1], "datatype": "INT64", "data": [1]},
        {"name": "Capital Gain", "shape": [1, 1], "datatype": "INT64", "data": [0]},
        {"name": "Capital Loss", "shape": [1, 1], "datatype": "INT64", "data": [0]},
        {"name": "Hours per week", "shape": [1, 1], "datatype": "INT64", "data": [40]},
        {"name": "Country", "shape": [1, 1], "datatype": "INT64", "data": [9]}
    ]
}