Principle:Mlflow Mlflow Prediction Endpoint
| Knowledge Sources | |
|---|---|
| Domains | ML_Ops, Model_Serving |
| Last Updated | 2026-02-13 20:00 GMT |
Overview
The prediction endpoint principle defines how MLflow handles incoming inference requests through REST API endpoints, including input parsing, schema validation, and response formatting.
Description
When an MLflow model is served via HTTP, the core inference logic resides in a scoring server that receives raw request data, validates and transforms it into a format the model can consume, and returns structured prediction results. This prediction endpoint abstraction decouples the HTTP transport layer from the model's predict() method, allowing models to accept multiple input formats (JSON, CSV, Parquet) through a single unified API surface.
The prediction endpoint enforces the MLflow Model scoring protocol, which standardizes how clients submit data for inference. For JSON payloads, the protocol requires one of several structural keys -- dataframe_split, dataframe_records, instances, or inputs -- that define how the data should be deserialized into the internal representation (typically a pandas DataFrame or numpy array). This structured approach prevents ambiguity in data interpretation and provides clear error messages when payloads do not conform to the expected schema.
Beyond the primary /invocations route, the scoring server exposes auxiliary endpoints for operational concerns: /ping and /health for readiness checks, and /version for reporting the MLflow version. These endpoints follow cloud-native health check conventions and integrate with load balancers, container orchestrators, and monitoring systems that probe service health before routing traffic.
Usage
Use the prediction endpoint pattern whenever you need to understand or customize how MLflow processes inference requests. This is relevant when debugging malformed request errors, implementing custom input preprocessing, integrating MLflow-served models with API gateways, or validating that client applications are sending data in the correct format. The endpoint behavior is consistent across local serving, Docker containers, and cloud deployments.
Theoretical Basis
The prediction endpoint follows the request-response pattern fundamental to synchronous REST APIs. Each prediction is treated as an independent, stateless transaction: the client sends input data, the server computes a prediction, and the result is returned in the same connection. This simplicity enables standard HTTP tooling for load testing, caching, and retry logic.
Schema validation at the endpoint level implements the principle of fail-fast error handling. Rather than allowing malformed data to propagate into the model's predict method (where errors may be cryptic), the scoring server validates content types, checks structural keys, and enforces the model's input schema before invoking prediction. This shifts errors closer to their source and produces actionable error messages.
The support for multiple serialization formats (JSON, CSV, Parquet) reflects the diversity of ML client ecosystems. Data scientists may send JSON from a notebook, ETL pipelines may stream CSV, and batch systems may transmit Parquet. By handling format negotiation at the endpoint level via the Content-Type header, the model itself remains format-agnostic, improving reusability.