A bad API design can torpedo an otherwise good model. Users can't use what's hard to integrate.
Core Principles
- Simplicity: Minimize required parameters. Sensible defaults for everything
- Clarity: Clear documentation, consistent naming, obvious behavior
- Robustness: Handle invalid inputs gracefully. Return helpful error messages
- Versioning: Support old versions while introducing new ones
Essential Features
Input Validation: Check types, ranges, and required fields before passing to the model. Fail fast with clear error messages.
Batch Endpoints: Allow processing multiple samples at once. It's more efficient than sequential calls.
Async Support: For long-running predictions, return immediately with a job ID. Let clients poll for results.
Error Handling Strategy
Differentiate between client errors (400) and server errors (500). A bad input isn't a server failure.
Include helpful error messages. "Invalid input" is worse than useless. Say what field failed and why.
Log everything. Track failed requests, slow responses, and error patterns. Use these to improve both API and model.
Monitoring and Observability
- Latency percentiles (p50, p95, p99)
- Error rate by type
- Model drift signals
- Cost per prediction
Pro Tip: Design your API as if you were the first customer. If it's hard for you to use, it's hard for everyone.