Machine Learning for Backend Engineers: A Practical Introduction

February 5, 2018

Machine learning has moved from research labs to production systems. As backend engineers, we’re increasingly asked to integrate ML models, build ML pipelines, and support data science teams. You don’t need to become a data scientist, but understanding ML fundamentals makes you more effective.

Here’s a practical introduction focused on what backend engineers need to know.

Understanding the Landscape

Types of Machine Learning

Supervised Learning: Train on labeled examples to predict labels for new data.

Unsupervised Learning: Find patterns in unlabeled data.

Reinforcement Learning: Learn through trial and error with rewards.

As backend engineers, you’ll most commonly encounter supervised learning—models trained on historical data to make predictions on new data.

The ML Development Lifecycle

Data Collection → Data Preparation → Feature Engineering →
Model Training → Model Evaluation → Deployment → Monitoring

Data scientists focus on the middle steps. Backend engineers typically help with data collection, deployment, and monitoring. Understanding the full cycle helps you collaborate effectively.

Working with Data

Data Pipelines

ML models need data. Your job often includes building pipelines that:

# Example: Simple ETL for ML training data
def extract_user_features(user_id, db):
    """Extract features for training."""
    user = db.query(User).get(user_id)
    orders = db.query(Order).filter_by(user_id=user_id).all()

    return {
        'user_id': user_id,
        'account_age_days': (datetime.now() - user.created_at).days,
        'total_orders': len(orders),
        'total_spent': sum(o.total for o in orders),
        'avg_order_value': sum(o.total for o in orders) / len(orders) if orders else 0,
        'days_since_last_order': (datetime.now() - max(o.created_at for o in orders)).days if orders else None,
    }

Feature Stores

Feature stores centralize feature computation and storage:

Tools like Feast, Tecton, or custom solutions help manage features at scale.

Data Quality

Bad data creates bad models. Common issues:

Build data quality checks into your pipelines.

Model Serving

Serving Patterns

Batch Prediction:

# Batch prediction job
def batch_predict(model, feature_store, output_db):
    users = feature_store.get_all_users()
    for batch in chunks(users, 1000):
        features = feature_store.get_features(batch)
        predictions = model.predict(features)
        output_db.upsert_predictions(batch, predictions)

Real-Time Prediction:

# Real-time serving endpoint
@app.route('/predict', methods=['POST'])
def predict():
    features = extract_features(request.json)
    prediction = model.predict([features])
    return jsonify({'prediction': prediction[0]})

Model Serving Infrastructure

Option 1: Embedded in Application

Option 2: Model Server

Option 3: Managed Services

Latency Considerations

Model inference can be slow:

Strategies:

Deploying Models

Model Versioning

Models are artifacts that need versioning:

models/
  churn_prediction/
    v1/
      model.pkl
      metadata.json
    v2/
      model.pkl
      metadata.json

Track:

Tools like MLflow, DVC, or custom solutions help manage model lifecycle.

A/B Testing Models

New models need validation:

# Simple A/B testing
def get_prediction(user, models):
    if user.id % 100 < 10:  # 10% traffic to new model
        model = models['challenger']
        variant = 'challenger'
    else:
        model = models['champion']
        variant = 'champion'

    prediction = model.predict(user.features)
    log_prediction(user, prediction, variant)  # For analysis
    return prediction

Compare metrics between variants before full rollout.

Rollback Strategy

Models can fail in production:

Have a rollback plan:

Monitoring ML Systems

Model Performance Metrics

Track prediction quality:

These require ground truth labels, which may arrive with delay.

Operational Metrics

Standard service metrics apply:

Data Drift

Models assume production data matches training data. When it doesn’t:

# Simple drift detection
def check_feature_drift(current_batch, reference_stats):
    for feature in current_batch.columns:
        current_mean = current_batch[feature].mean()
        reference_mean = reference_stats[feature]['mean']
        reference_std = reference_stats[feature]['std']

        z_score = (current_mean - reference_mean) / reference_std
        if abs(z_score) > 3:
            alert(f"Drift detected in {feature}: z-score={z_score}")

Monitor for:

Prediction Monitoring

Track what your model predicts:

Unusual patterns may indicate data issues or model degradation.

Common Pitfalls

Training-Serving Skew

Feature computation must be identical between training and serving. Common sources of skew:

Solution: Use the same feature computation code for both.

Leaky Features

Features that contain information about the target that wouldn’t be available at prediction time:

These create models that work perfectly in training and fail in production.

Stale Models

Models degrade over time as the world changes:

Plan for regular retraining and monitoring.

Collaboration with Data Scientists

What Data Scientists Need from You

What You Need from Data Scientists

Shared Responsibilities

Clear ownership prevents gaps.

Key Takeaways

ML is becoming a standard tool in the backend engineer’s toolkit. You don’t need to train models, but you need to deploy, serve, and monitor them reliably. These skills make you invaluable as organizations adopt ML.