---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: c513ba678e39d2d340958e96034915ce1bd9c0a8723e79eb8335b4e6205197bd
  pageDirectory: concepts
  sources:
    - regression-with-automl-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - automl-warnings-and-diagnostics
    - Diagnostics and AutoML Warnings
    - AWAD
  citations:
    - file: regression-with-automl-databricks-on-aws.md
title: AutoML Warnings and Diagnostics
description: AutoML feature that displays warnings for potential dataset issues such as unsupported column types or high cardinality columns
tags:
  - automl
  - debugging
  - databricks
timestamp: "2026-06-19T20:13:02.544Z"
---

## AutoML Warnings and Diagnostics

The **AutoML Warnings and Diagnostics** feature in Databricks displays potential issues or errors detected in the dataset during an AutoML experiment. Starting with Databricks Runtime 10.1 ML, AutoML automatically evaluates the dataset and surfaces warnings for common data quality problems, such as unsupported column types or high cardinality columns. These warnings help users identify and address issues before or after training completes. ^[regression-with-automl-databricks-on-aws.md]

Warnings are accessible on both the training page (while the experiment is running) and the experiment page after the experiment finishes. To view them, click the **Warnings** tab. Autogenerated notebooks for data exploration and best-model training also incorporate any identified issues. ^[regression-with-automl-databricks-on-aws.md]

### Limitations

Databricks states that the warning system is best-effort: “Databricks does its best to indicate potential errors or issues. However, this may not be comprehensive and may not capture the issues or errors you may be searching for.” Users should still perform independent validation of dataset suitability for the chosen problem type. ^[regression-with-automl-databricks-on-aws.md]

### Related Concepts

- AutoML – Overview of automated machine learning on Databricks.
- Regression with AutoML – Configuration and results for regression experiments.
- [Data Profiling](/concepts/data-profiling.md) – Broader dataset analysis and quality checks.
- Data Exploration Notebook – Auto-generated notebook that may include warnings.

### Sources

- regression-with-automl-databricks-on-aws.md

# Citations

1. [regression-with-automl-databricks-on-aws.md](/references/regression-with-automl-databricks-on-aws-cc5aa3d0.md)
