---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: aca1d386021263cabc88a31a6f69554eeb907ed7a3725d85d550e309769227c8
  pageDirectory: concepts
  sources:
    - forecasting-with-automl-classic-compute-databricks-on-aws.md
  confidence: 1
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - automl-result-monitoring-and-model-deployment
    - Model Deployment and AutoML Result Monitoring
    - ARMAMD
  citations:
    - file: forecasting-with-automl-classic-compute-databricks-on-aws.md
title: AutoML Result Monitoring and Model Deployment
description: Workflow for monitoring experiment progress, viewing warnings, reviewing best model notebooks, and registering/deploying models to Unity Catalog or Model Registry via the AutoML UI.
tags:
  - automl
  - model-deployment
  - mlflow
timestamp: "2026-06-19T10:37:30.815Z"
---

# AutoML Result Monitoring and Model Deployment

**AutoML Result Monitoring and Model Deployment** refers to the post-training workflow for examining AutoML experiment runs and promoting the best-performing model to a production serving endpoint. These steps are available both during and after a forecasting experiment on Databricks.

## Monitor Experiment Progress[​](#monitor-experiment-progress "Direct link to Monitor experiment progress")

After starting an AutoML experiment from the UI, the training page displays a runs table that you can refresh to see the latest status. From this page, you can:

- Stop the experiment at any time.
- Open the data exploration notebook.
- Monitor runs as they complete.
- Navigate to the run page for any individual trial. ^[forecasting-with-automl-classic-compute-databricks-on-aws.md]

When using Databricks Runtime 10.1 ML and above, AutoML displays warnings for potential dataset issues such as unsupported column types or high cardinality columns. These warnings appear in a **Warnings** tab on the training page and on the experiment page after the experiment completes. ^[forecasting-with-automl-classic-compute-databricks-on-aws.md]

## View Experiment Results[​](#view-experiment-results "Direct link to View experiment results")

Once the experiment finishes, the runs table shows all trials sorted by the primary metric. From this table you can:

- **Register and deploy** one of the models (see next section).
- **View notebook for best model** – review and edit the notebook that created the best model.
- **View data exploration notebook** – open the data exploration notebook generated by AutoML.
- **Search, filter, and sort** the runs in the table.
- **See details for any run** – click into an [MLflow Run](/concepts/mlflow-run.md) to inspect parameters, metrics, tags, and artifacts. The generated notebook containing the source code for that trial is saved in the **Artifacts** section of the run page and can be downloaded and imported into the workspace (if downloading artifacts is enabled by workspace administrators). ^[forecasting-with-automl-classic-compute-databricks-on-aws.md]

All experiment results, including both the data exploration and training notebooks, are stored in a `databricks_automl` folder inside the home folder of the user who ran the experiment. You can return to the experiment later by finding it in the table on the [MLflow Experiments](/concepts/mlflow-experiment.md) page. ^[forecasting-with-automl-classic-compute-databricks-on-aws.md]

## Register and Deploy a Model[​](#register-and-deploy-a-model "Direct link to register-and-deploy-a-model")

After a run completes, the top row of the runs table shows the best model based on the primary metric. To register and deploy it:

1. Select the link in the **Models** column for the model you want to register.
2. Click the **register model button** to register the model to [Unity Catalog](/concepts/unity-catalog.md) or the [Model Registry](/concepts/mlflow-model-registry.md). Databricks recommends registering models to Unity Catalog for the latest features.
3. After registration, you can deploy the model to a [custom model serving endpoint](/concepts/custom-model-serving-endpoint-support.md). ^[forecasting-with-automl-classic-compute-databricks-on-aws.md]

### Troubleshooting: pandas Version Mismatch[​](#troubleshooting-pandas-version-mismatch "Direct link to Troubleshooting: pandas version mismatch")

When serving an AutoML-built model with [Model Serving](/concepts/model-serving.md), you may encounter the error `No module named 'pandas.core.indexes.numeric'.` This is caused by an incompatible `pandas` version between the AutoML training environment and the serving endpoint. To resolve this:

1. Run the [add-pandas-dependency.py](https://docs.databricks.com/aws/en/assets/files/add-pandas-dependency-4808dc9dcdfb035bdca6ebce6b86d719.py) script, editing it to include the `run_id` of the [MLflow Run](/concepts/mlflow-run.md) where your model was logged.
2. Re-register the model to Unity Catalog or the model registry.
3. Try serving the new version of the model. ^[forecasting-with-automl-classic-compute-databricks-on-aws.md]

## Related Concepts[​](#related-concepts "Direct link to Related Concepts")

- [AutoML (classic compute)](/concepts/automl-forecasting-classic-compute.md) – The overall workflow and configuration options.
- [MLflow Experiments](/concepts/mlflow-experiment.md) – Central repository for AutoML trial runs.
- [Model Registry](/concepts/mlflow-model-registry.md) – Lifecycle management for registered models.
- [Model Serving](/concepts/model-serving.md) – Production deployment of MLflow models.
- Forecasting with AutoML – Specific guidance for time-series forecasting.
- [Databricks Runtime for Machine Learning](/concepts/databricks-runtime-for-machine-learning.md) – Required runtime for AutoML features.

## Sources[​](#sources "Direct link to Sources")

- forecasting-with-automl-classic-compute-databricks-on-aws.md

# Citations

1. [forecasting-with-automl-classic-compute-databricks-on-aws.md](/references/forecasting-with-automl-classic-compute-databricks-on-aws-20de86d0.md)
