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¶

In this demo we’re naming the pipeline income You also need to specify the type as Seldon ML Pipeline

Name your pipeline

3. Select model runtime¶

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

Select model runtime

4. Provide model URI¶

In this demo we’re using this model URI: gs://seldon-models/scv2/examples/mlserver_1.2.3/income/classifier

Provide model URI

Create an explainer¶

1. Select your pipeline¶

Select your pipeline

2. Navigate to the explainer section¶

There you’ll find the add 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¶

Explainer model URI gs://seldon-models/scv2/examples/mlserver_1.2.3/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]}
    ]
}