The gap between a working AI prototype and a production-ready system is where most AI projects fail. Only 22% of AI projects make it to production. Here's how to be in that 22%.
Why AI Projects Fail at Scale
- Infrastructure mismatch — Jupyter notebooks don't scale
- Data pipeline fragility — Manual data processing breaks in production
- No monitoring — Model performance degrades silently
- Lack of versioning — Can't reproduce or rollback model versions
The Production Readiness Checklist
1. Containerize Everything
Wrap your model, dependencies, and serving logic in Docker containers. This ensures reproducibility across environments.
2. Build CI/CD for ML
Automate model training, evaluation, and deployment. Use tools like MLflow or Weights & Biases for experiment tracking.
3. Implement Model Monitoring
Track prediction accuracy, latency, and data drift in real-time. Set up alerts when metrics deviate from baselines.
4. Plan for Failure
What happens when your model returns garbage? Implement fallback logic, graceful degradation, and human-in-the-loop escalation.
Iedeo has helped companies take AI from prototype to production across healthcare, finance, and e-commerce. Let's discuss your project.