Deploying the Trained XGBoost Model as a Real-Time Endpoint

After successfully training our XGBoost model, the next step is to deploy it to an Amazon SageMaker endpoint for real-time inference. This deployment allows the model to serve predictions via API requests, making it suitable for applications that require low-latency predictions.

Prerequisites

Before deploying the model, ensure you’ve completed the model training steps in my previous article, Building a Machine Learning Model with AWS SageMaker. This guide provides foundational steps needed for training an XGBoost model in SageMaker before deployment.

Deploy the Trained XGBoost Model

To deploy the model, we use thedeploy()method, which sets up a fully managed endpoint. Here, you can control the scalability and resource allocation by specifying parameters such asinitial_instance_countandinstance_type.

xgb_predictor = xgb.deploy(
    initial_instance_count=1,
    instance_type='ml.m4.xlarge'
)

This code will create an endpoint with a unique name, allowing your model to be accessed for predictions.

Configure Serializer for Model Endpoint Input Format

Once the model is deployed, it's essential to configure how input data is formatted when sent to the endpoint. By setting the serializer toCSVSerializer, we ensure that the input data is converted to CSV format, which aligns with what the trained XGBoost model expects.

xgb_predictor.serializer = sagemaker.serializers.CSVSerializer()

This configuration guarantees smooth data processing and accurate predictions.

Load Test Data for Inference: Features and Labels

Next, we need to load the test data for inference from S3. This includes two CSV files: one for feature data (test_script_x.csv) and another for actual labels (test_script_y.csv). By specifyingheader=None, we ensure that these files are read correctly without assuming any header row.

test_data_x = pd.read_csv(os.path.join(test_path, 'test_script_x.csv'), header=None)
test_data_y = pd.read_csv(os.path.join(test_path, 'test_script_y.csv'), header=None)

These dataframes will be used for evaluating the model's performance or making predictions.

Batch Prediction for Large Datasets Using SageMaker Endpoint

When dealing with large datasets, it's efficient to split the input data into smaller batches before sending it to the endpoint. This approach prevents overwhelming system resources and allows for smoother processing.

def predict(data, predictor, rows=500):
    split_array = np.array_split(data, int(data.shape[0] / float(rows) + 1))
    predictions = ''

    for array in split_array:
        predictions = ','.join([predictions, predictor.predict(array).decode('utf-8')])

    return np.fromstring(predictions[1:], sep=',')

You can call this function ontest_data_xto get predictions using your trained XGBoost model:

predictions = predict(test_data_x, xgb_predictor)

Generate Confusion Matrix for Model Predictions

To evaluate the model's performance, we can generate a confusion matrix that compares predicted values with actual labels from the test set. This matrix provides insights into how many instances were correctly or incorrectly classified.

pd.crosstab(index=test_data_y[0], columns=np.round(predictions), 
            rownames=['actuals'], colnames=['predictions'])

The resulting confusion matrix will help you understand your model’s classification accuracy better.

Conclusion

In this section of our blog article, we covered how to deploy a trained XGBoost model as a real-time endpoint using AWS SageMaker. By following these steps, you can efficiently manage predictions in a production setting.For further details and code examples, feel free to explore my GitHub repository here.With these tools at your disposal, you can leverage machine learning models effectively in real-world applications!