Building ML Systems at Scale

Machine learning systems in production are fundamentally different from research prototypes. Here are the key considerations I've learned from building and maintaining ML systems at scale.

The Training-Serving Skew Problem

One of the most common issues in production ML is the training-serving skew. This happens when the data distribution at inference time differs from training time.

# Example of feature transformation that must be consistent
def preprocess_features(raw_data):
    # This exact logic must be used in both training and serving
    normalized = (raw_data - TRAINING_MEAN) / TRAINING_STD
    return normalized

Monitoring is Not Optional

You need to monitor:

• Input data distributions - detect drift early
• Model predictions - track confidence scores
• Downstream metrics - measure actual business impact

Versioning Everything

Version your:

1. Training data
2. Model artifacts
3. Feature transformations
4. Configuration files

This enables reproducibility and makes debugging much easier when issues arise.