Most ML engineers build great models and terrible APIs.

I've consumed ML APIs that return predictions in five different formats depending on input. That require 30 lines of preprocessing before you can send a request. That return cryptic error messages like "dimension mismatch" when you send a string instead of an integer.

Your model is only as good as the interface people use to access it.

Good ML API design: consistent input/output schemas validated with Pydantic. Meaningful error messages that tell the caller what went wrong AND how to fix it. Versioned endpoints so updates don't break existing users. Batch prediction endpoints for bulk processing. Health check and readiness endpoints. Latency headers so callers can monitor performance.

I've started designing my APIs before my models. Define the contract first — what goes in, what comes out, what errors look like. Then build the model to fit the contract.

The best ML API I ever used felt like calling a function. No surprises. No guessing. No reading source code to figure out the expected format.

That's the standard we should all aim for.