Training a model is the easy part. Putting it into production and keeping it there is where the real work begins.
The MLOps Challenge
Production ML requires:
- Reproducible training pipelines
- Model versioning and lineage
- Automated retraining
- Monitoring for drift
- A/B testing capabilities
Serving Patterns
Batch Inference: Process large datasets periodically
Real-time Inference: Low-latency predictions for user requests
Edge Inference: Run models on devices
Monitoring Model Health
# Track prediction distribution
def monitor_predictions(predictions):
wandb.log({
"prediction_mean": predictions.mean(),
"prediction_std": predictions.std(),
"drift_score": calculate_drift(predictions)
})
Model Governance
Who approved this model? What data was it trained on? When was it last validated? These questions need answers before production deployment.