Machine Learning Production: MLOps Best Practices

Deploying machine learning models to production requires sophisticated MLOps practices to ensure reliability, performance, and continuous improvement.

The MLOps Lifecycle

Successful ML production deployment follows a structured lifecycle:

1. Model Development

• Data collection and preprocessing
• Feature engineering
• Model training and validation
• Hyperparameter tuning

2. Model Deployment

# MLOps deployment pipeline
from mlflow import deploy

def deploy_model(model_version, environment):
    model = mlflow.load_model(f"models:/{model_version}")
    
    # Validate model performance
    if validate_model(model):
        deploy.to_production(model, environment)
        monitor_model_performance(model)
    else:
        rollback_deployment()

Model Monitoring

Continuous monitoring is critical for production ML systems:

Metric	Threshold	Action
Prediction Latency	< 100ms	Alert if exceeded
Model Accuracy	> 95%	Retrain if below
Data Drift	< 5%	Investigate if high
Feature Drift	< 3%	Update features

Version Control for Models

1. Track model versions with metadata
2. Maintain training data lineage
3. Document model performance metrics
4. Enable quick rollback capabilities

Continuous Improvement

ML models are not static artifacts—they require continuous monitoring, retraining, and improvement to maintain performance in production.