Create an API endpoint to serve the prospect scores in real time

You now have attribute values in your Profiles Store, and a trained ML model. The next step is to create and deploy an endpoint to serve attributes and predictions.

It needs to do the following:

• Get calculated attribute values from Signals
• Score the values using the model
• Return the results

This tutorial uses Flask together with TryCloudflare tunnels to expose the API endpoint outside of the Colab notebook environment.

Install dependencies

Run %pip install flask-cloudflared to install the required libraries.

Create ML scoring endpoint

Start by loading your model, and defining the methods needed to retrieve and process your Signals data.

python

from flask import Flask, request
from flask_cloudflared import run_with_cloudflared

# Define and launch Colab API Proxy
app = Flask(__name__)
run_with_cloudflared(app) # Open Cloudflared demo tunnel

# Load model
model = joblib.load("model.joblib")

# Process the Signals data for individual domain_userids
def get_duid_values(duid: str):
    # Retrieve attributes from Signals
    response = sp_signals.get_service_attributes(
        name="prospect_scoring_tutorial_service",
        attribute_key="domain_userid",
        identifier=duid,
    )
    signals_df = pd.DataFrame([response])
    # Prepare ML dataframe
    ml_df = signals_df.fillna(0).reindex(columns=x_columns, fill_value=0)
    return (signals_df, ml_df)

# Score the data against the model
def get_predictions(df):
    return float(model.predict_proba(df)[:, 1][0])

@app.route("/predict", methods=['POST'])
def predict():
    input_dict = request.get_json() # Parse JSON input

    # Get Signals data and prepare dataframe for scoring
    signals_df, ml_df = get_duid_values(input_dict['domain_userid'])

    # Score dataframe using the trained model
    prediction = get_predictions(ml_df)

    # Return the result
    print(f"P: {round(prediction, 4)} - {input_dict}")
    return {
        "signals": signals_df.to_dict(orient='records')[0],
        "scoring_attributes": ml_df.to_dict(orient='records')[0],
        "score": prediction
    }

app.run()

This /predict endpoint does four things:

1. Receives a domain_userid
2. Calls the Signals API to get the current attribute values, using the prospect_scoring_tutorial_service service
3. Scores the attribute values using the ML model
4. Returns Signals attributes, and ML prediction score