Here’s a simple checklist for people who deploy machine learning models to production. Based on my personal experience.
- Identify Bias: Will the bias affect users? Discuss with the product team.
- Performance Profile: List your hardware requirements: CPU/GPU/RAM/DISK, sustaining usage or burst?
- Model file size: Avoid network congestion. Decide on push-based or pull-based deployment.
- Continuous testing: Test the model on real-world data. Check for over-fitting.
- Prepare for rollback.
- Use canary deployment if possible.
- Log Collection: collect log for debugging, remove personal information, sample if needed.
- Error handling: No model is perfect. Respond to user feedback, fast.
- Emergency response: Be prepare to fix the application with heuristic rules.
- Observability: Detect concept drift in the real world asap.
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