AI Development Best Practices

Building production-ready AI applications requires more than just training a model. Here are the key practices I've learned from years of developing AI systems.

1. Data Quality First

The foundation of any AI system is high-quality data. Here's what I focus on:

• Data validation pipelines to catch issues early
• Consistent preprocessing across training and inference
• Data versioning to track changes over time

2. Model Monitoring

Once your model is in production, monitoring becomes critical:

• Track data drift to detect when your model's assumptions break
• Monitor prediction confidence to catch edge cases
• Set up performance alerts for key metrics

3. Testing Strategies

AI applications need comprehensive testing:

• Unit tests for data processing functions
• Integration tests for model pipelines
• A/B testing for model performance comparison

Code Example

Here's a simple monitoring function I use:

def monitor_prediction_drift(predictions, baseline_stats):
    current_mean = np.mean(predictions)
    drift_threshold = 0.1
    
    if abs(current_mean - baseline_stats['mean']) > drift_threshold:
        alert_team("Prediction drift detected!")
    
    return current_mean

Key Takeaways

1. Treat AI development like software engineering
2. Automate everything you can
3. Plan for failure scenarios
4. Keep humans in the loop for critical decisions

Building reliable AI systems is challenging, but following these practices will help you avoid common pitfalls and build systems that actually work in production.