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    - debugging-guide-for-model-serving-databricks-on-aws.md
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  aliases:
    - pre-deployment-validation-for-model-serving
    - PVFMS
    - Pre‑deployment validation for Model Serving
    - Pre-Deployment Validation
    - Pre-deployment Validation
    - Pre-deployment model validation
    - Pre-deployment validation
    - pre-deployment validation
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    - file: debugging-guide-for-model-serving-databricks-on-aws.md
      start: 8
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    - file: 13-17
title: Pre-deployment Validation for Model Serving
description: Practices for validating model serving configurations before deployment to catch common issues early, preventing build and runtime failures.
tags:
  - model-serving
  - validation
  - deployment
timestamp: "2026-06-19T14:56:47.924Z"
---

---

title: Pre-deployment Validation for Model Serving
summary: A recommended first step to catch common problems before deploying a model serving endpoint on Databricks.
sources:
  - debugging-guide-for-model-serving-databricks-on-aws.md
kind: concept
createdAt: "2026-06-20T09:00:00.000Z"
updatedAt: "2026-06-20T09:00:00.000Z"
tags:
  - model-serving
  - deployment
  - validation
  - troubleshooting
aliases:
  - pre-deployment-validation-for-model-serving
  - pdvms
confidence: 0.9
provenanceState: extracted
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---

# Pre-deployment Validation for Model Serving

**Pre-deployment Validation** is a recommended first step when deploying a [Model Serving](/concepts/model-serving.md) endpoint on Databricks. It helps identify and resolve common issues before the endpoint is created or updated, reducing the time spent debugging after deployment. ^[debugging-guide-for-model-serving-databricks-on-aws.md:8-10]

## Overview

The pre-deployment validation process is designed to catch problems such as missing dependencies, incompatible model signatures, or incorrect environment configurations before they cause build or runtime failures. Databricks advises starting with pre-deployment validation whenever deployment issues are encountered. ^[debugging-guide-for-model-serving-databricks-on-aws.md:8-10]

## Use as a First Step

If you are having trouble deploying a model serving endpoint, the recommended workflow is:

1. **Run pre-deployment validation** to catch common problems early.
2. If the validation passes but problems persist, proceed to the Debugging Guide for Model Serving for detailed troubleshooting steps.

The debugging guide covers container build failures, runtime errors, and concurrency limits. ^[debugging-guide-for-model-serving-databricks-on-aws.md:8-10, 13-17]

## Related Concepts

- [Model Serving](/concepts/model-serving.md) – Overview of serving endpoints on Databricks.
- Debugging Guide for Model Serving – Detailed steps for troubleshooting after validation.
- Container Build – The image creation step that validation checks.
- [MLflow](/concepts/mlflow.md) – Model logging and packaging that affects deployment.
- [Inference Tables](/concepts/inference-tables.md) – Logs for root cause analysis of failed requests.

## Sources

- debugging-guide-for-model-serving-databricks-on-aws.md

# Citations

1. [debugging-guide-for-model-serving-databricks-on-aws.md:8-10](/references/debugging-guide-for-model-serving-databricks-on-aws-67072c6a.md)
2. 13-17
