---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: f3eefc74c1458db5827d6fcba29a96039e2ec7207d4c85a06d8dcedd90112da5
  pageDirectory: concepts
  sources:
    - deploy-python-code-with-model-serving-databricks-on-aws.md
  confidence: 0.85
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - mlflow-pyfunc-model-validation
    - MPMV
  citations:
    - file: deploy-python-code-with-model-serving-databricks-on-aws.md
title: MLflow PyFunc Model Validation
description: The practice of using mlflow.models.predict to validate custom pyfunc models before deploying them to a serving endpoint, ensuring the model is capable of being served.
tags:
  - mlflow
  - testing
  - deployment
timestamp: "2026-06-19T18:30:45.750Z"
---

# MLflow PyFunc Model Validation

**MLflow PyFunc Model Validation** refers to the process of verifying that a custom MLflow PyFunc model is capable of being served before it is deployed to a [Model Serving](/concepts/model-serving.md) endpoint. Validation helps catch runtime errors early, ensuring that the model’s `predict` method and any associated preprocessing or postprocessing logic behave correctly with realistic inputs.

## Overview

When you construct a custom `pyfunc` model — for example, one that requires preprocessing, uses a non-native framework, or applies per-request branching — it is beneficial to validate the model prior to deployment. MLflow provides the `mlflow.models.predict` API for this purpose. By calling this function with sample input data, you can confirm that the model loads correctly and produces expected outputs without requiring a full serving infrastructure. ^[deploy-python-code-with-model-serving-databricks-on-aws.md]

## Why Validate?

Validation serves as a lightweight integration test for your model. It verifies that:

- The `load_context` method (used to initialize artifacts such as tokenizers, model weights, or shared code modules) runs successfully.
- The `predict` method accepts the input format you intend and returns output in the expected shape.
- Any dependencies declared in `pip_requirements` (including `mlflow` instead of `mlflow-skinny`) are compatible with the model’s runtime.

This step is especially important when deploying to Databricks Model Serving, where the serving environment must build a container image from the model’s `conda.yaml` and `pip_requirements`. ^[deploy-python-code-with-model-serving-databricks-on-aws.md]

## Usage

The MLflow documentation recommends using `mlflow.models.predict` to validate models before deployment. The typical workflow is:

1. Log your custom `pyfunc` model using `mlflow.pyfunc.log_model()`.
2. Load the logged model (or use the logged model URI).
3. Call `mlflow.models.predict` with a representative input to simulate serving.
4. Inspect the output for correctness.

If the validation succeeds, you can register the model to [Unity Catalog](/concepts/unity-catalog.md) or the [Workspace Registry](/concepts/workspace-model-registry.md) and create a serving endpoint with confidence. ^[deploy-python-code-with-model-serving-databricks-on-aws.md]

## Related Concepts

- [Custom MLflow PyFunc Model](/concepts/custom-mlflow-pythonmodel.md) – The model framework that validation applies to.
- [Model Serving](/concepts/model-serving.md) – The deployment target for validated models.
- [mlflow models predict](/concepts/mlflowmodelspredict-api.md) – The API used for validation (see the [MLflow documentation](https://mlflow.org/docs/latest/models.html#validate-models-before-deployment)).
- [MLflow Python Function (pyfunc)](/concepts/mlflow-pyfunc-python-function.md) – The flexible model packaging format.

## Sources

- deploy-python-code-with-model-serving-databricks-on-aws.md

# Citations

1. [deploy-python-code-with-model-serving-databricks-on-aws.md](/references/deploy-python-code-with-model-serving-databricks-on-aws-84536f0c.md)
