I've seen AI deployments succeed magnificently and fail spectacularly. The difference usually isn't the model—it's the deployment strategy. Here's what I've learned about getting AI models into production and keeping them running.
The Batch vs. Real-Time Decision
First, understand your use case. Are you making predictions in real-time (like fraud detection on each transaction) or in batches (like generating daily recommendations)?
Batch inference is simpler. Run your model on a schedule, save predictions, serve from cache. Less complex infrastructure, easier monitoring.
Real-time inference requires always-on infrastructure, but enables use cases that batch can't handle. Think carefully about latency requirements.
Deployment Patterns That Work
Blue-Green Deployment: Run two identical environments. Deploy new model to the inactive one, test it, then switch traffic. Instant rollback if things go wrong.
Canary Deployment: Gradually shift traffic to the new model. Start with 1%, monitor for problems, slowly increase. Catches issues before they affect everyone.
A/B Testing: Route different users to different model versions. Compare performance and pick the winner. Essential for comparing models in the real world.
Scaling Considerations
Think about load from day one. Can your inference server handle 10 requests per second? 1,000? What happens at 10,000?
Horizontal scaling (more servers) is usually easier than vertical (bigger servers). Container orchestration with Kubernetes has become the standard for this.
Consider model distillation—training a smaller "student" model from a larger "teacher" can dramatically improve inference speed with minimal accuracy loss.
The Importance of Testing
Your model needs integration tests just like any software. Test:
- Input validation (what happens with malformed input?)
- Output format (does the API response match expectations?)
- Latency under load
- Error handling (what happens when the model fails?)
Rollback Strategy
Assume things will go wrong. Have a plan to revert. In practice, this means:
- Version everything: model, code, data, config
- Keep previous model versions deployable
- Monitor the switchover closely
- Have a communication plan for outages
Deployment isn't a one-time event—it's the start of an ongoing relationship with your model in production. Plan for the long haul.