Data Science Workflow: From Problem to Solution – A Complete Step-by-Step Journey for Beginners

📗 Chapter 10: Monitoring, Maintenance, and Reporting

Keeping Your Machine Learning Model Healthy, Relevant, and Actionable

🧠 Introduction

Once your machine learning model is deployed, the job isn’t done. In fact, it’s just beginning.

A model that works today might become useless tomorrow if not monitored, maintained, and reported properly.

This chapter focuses on:

• Real-time model monitoring (accuracy, drift, latency)
• Scheduled model maintenance and retraining
• Generating reports for technical and business teams
• Tools for observability and alerting
• Best practices for long-term success

Let’s explore how to ensure your model keeps delivering value over time.

🔄 1. Why Monitoring Matters

Without Monitoring:

📡 2. Key Monitoring Metrics

🛠️ 3. How to Implement Monitoring

✅ Logging Predictions

python

import datetime

import json

def log_prediction(input_data, prediction):

    log = {

        'timestamp': str(datetime.datetime.now()),

        'input': input_data,

        'prediction': prediction

    }

    with open('logs.jsonl', 'a') as f:

        f.write(json.dumps(log) + '\n')

✅ Track API Health with Prometheus + Grafana

• Use Prometheus to collect metrics (latency, error rates)
• Use Grafana to visualize them in real time

bash

# Example Flask app endpoint

@app.route("/health")

def health():

    return {"status": "ok"}, 200

Integrate Prometheus exporters like prometheus_flask_exporter.

✅ Alerting with Watchdog or Sentry

Get alerts when:

• Accuracy drops
• Response time spikes
• Exceptions occur

bash

# Sample email alert trigger in Python

if accuracy < 0.75:

    send_email("Model accuracy dropped below threshold!")

📈 4. Detecting Model Drift

✅ What is Drift?

✅ Tools to Detect Drift:

♻️ 5. Model Maintenance Best Practices

🔁 Retraining Triggers:

✅ Retraining Script Example

python

def retrain_model():

    new_data = load_new_data()

    X_train, y_train = preprocess(new_data)

    model.fit(X_train, y_train)

    joblib.dump(model, 'model_updated.pkl')

Schedule this with cron or use Airflow for orchestration.

📄 6. Reporting for Stakeholders

Machine learning results should be made digestible for non-technical audiences.

✅ Include in Your Report:

📊 Visualization Tips

• Use bar charts for feature importance
• Use line charts for accuracy trends
• Use heatmaps for correlation or drift analysis

python

import seaborn as sns

sns.heatmap(df.corr(), annot=True)

📁 Report Format Options

💡 7. Tools and Services for Maintenance

🔄 8. Automating the MLOps Loop

Create a CI/CD pipeline for ML: