The main challenge is that a prototype focuses on accuracy in isolation, while production requires reliability, scalability, monitoring, and integration into business systems.
Data-related issues
- Data drift: Distribution of incoming data differs from training data.
- Concept drift: Relationship between inputs and outputs changes over time.
- Data quality: Missing, delayed, or corrupted data in live systems.
- Feature availability: Features used in prototyping may not exist or be delayed in production.
Scalability & performance
- Prototype may work on a subset, but production must handle larger volumes with low latency.
- Need for real-time processing vs batch pipelines.
- Resource constraints (CPU/GPU/memory).
Model Robustness
- Prototype may overfit; needs retraining and validation under production conditions.
- Handling edge cases and rare events.
Integration with systems
- APIs, databases, or event-driven architectures may need redesign.
- Version control for models and dependencies.
- Ensuring reproducibility between dev and prod environments.
Monitoring & maintenance
- Must track model accuracy, latency, and drift.
- Establish alerting and retraining triggers.
- Logging predictions for auditing and debugging.
Governance & security
- Compliance with regulations (GDPR, HIPAA, etc.).
- Access control for models and data.
- Ethical considerations (bias, fairness, explainability).