Question

A Generative AI Engineer is logging a RAG chain with MLflow before deploying it to a Model Serving endpoint. They pass an input_example when calling mlflow.langchain.log_model() but do not explicitly define a ModelSignature. When they later query the deployed endpoint, some client requests are rejected due to schema mismatch, while the engineer expected MLflow to have captured a signature automatically. What is the most accurate explanation of MLflow's behavior here and how to make the endpoint's input contract reliable?

Accepted Answer

MLflow can infer a signature from the input_example, but inference from a single example may not capture all valid field types or optional keys; explicitly defining and logging a ModelSignature makes the input/output contract deterministic for serving.

Answer

MLflow never infers a signature from an input_example, so no signature was stored at all; the only fix is to disable schema enforcement on the serving endpoint.

Answer

Providing an input_example automatically guarantees a complete and correct signature for all future requests, so the mismatch must be caused by the endpoint's autoscaling configuration rather than the schema.

Answer

Signatures are only supported for pyfunc models, not for the langchain flavor, so the engineer must re-log the chain as a custom pyfunc model to get any schema enforcement.