Deployment

Serving Infrastructure

Component	Purpose
Model API	RESTful endpoints for inference
Batch Processing	Scheduled runs for large datasets
Real-time Inference	Low-latency predictions
Model Versioning	Track and manage versions

Environments

Environment	URL
Development	https://model-api-dev.credplatform.com/
Staging	https://model-api-staging.credplatform.com/
Production	https://model-api.credplatform.com/

Monitoring

• Model Performance - Track accuracy and drift
• System Health - Monitor API response times
• Data Quality - Validate input data quality
• Alerting - Notify on model degradation

---

API Usage

Authentication

import requests

headers = {
    'Authorization': f'Bearer {api_token}',
    'Content-Type': 'application/json'
}

Model Inference

response = requests.post(
    'https://model-api.credplatform.com/predict',
    headers=headers,
    json={
        'model_id': 'predictive_analytics_v1',
        'input_data': {
            'feature1': 100,
            'feature2': 'category_a',
            'feature3': [1, 2, 3]
        }
    }
)

predictions = response.json()

Batch Processing

# Upload data for batch processing
with open('data/batch_input.csv', 'rb') as f:
    response = requests.post(
        'https://model-api.credplatform.com/batch',
        headers=headers,
        files={'file': f},
        data={'model_id': 'predictive_analytics_v1'}
    )

job_id = response.json()['job_id']

# Check job status
status_response = requests.get(
    f'https://model-api.credplatform.com/jobs/{job_id}',
    headers=headers
)