# Custom model training data assignment update

> Custom model training data assignment update - Describes the conversion process for custom model
> training data assignment and the removed method for assigning training data directly to a custom
> model.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:10.013828+00:00` (UTC).

## Primary page

- [Custom model training data assignment update](https://docs.datarobot.com/en/docs/release/deprecations-and-migrations/cus-model-training-data.html): Full documentation for this topic (HTML).

## Related documentation

- [AI Platform releases](https://docs.datarobot.com/en/docs/release/index.html): Linked from this page.
- [Deprecations and migrations](https://docs.datarobot.com/en/docs/release/deprecations-and-migrations/index.html): Linked from this page.
- [deprecated in DataRobot version 9.1](https://docs.datarobot.com/en/docs/release/archive-release-notes/pre-10/v9.1/v9.1.0-mlops.html#assign-training-data-to-a-custom-model-version): Linked from this page.
- [additional announcement in DataRobot version 10.0](https://docs.datarobot.com/en/docs/release/archive-release-notes/v10.0/v10.0.0-mlops.html#custom-model-training-data-assignment-update): Linked from this page.
- [removed in DataRobot version 10.1](https://docs.datarobot.com/en/docs/release/archive-release-notes/v10.1/v10.1.0-mlops.html#custom-model-training-data-assignment-update): Linked from this page.
- [deprecated in March 2023](https://docs.datarobot.com/en/docs/release/cloud-history/2023-announce/march2023-announce.html#assign-training-data-to-a-custom-model-version): Linked from this page.
- [removed in April 2024](https://docs.datarobot.com/en/docs/release/cloud-history/index.html#custom-model-training-data-assignment-update): Linked from this page.
- [documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-training-data.html): Linked from this page.
- [testing a custom model](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-test.html): Linked from this page.
- [assemble a custom model](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-workshop/nxt-create-custom-model.html): Linked from this page.
- [data drift](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/data-drift.html): Linked from this page.
- [accuracy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/deploy-accuracy.html): Linked from this page.

## Documentation content

**Self-Managed:**
To enable feature drift tracking for a model deployment, you must add training data. Previously, you assigned training data directly to a custom model, meaning every version of that model used the same data; however, this assignment method was [deprecated in DataRobot version 9.1](https://docs.datarobot.com/en/docs/release/archive-release-notes/pre-10/v9.1/v9.1.0-mlops.html#assign-training-data-to-a-custom-model-version) (with an [additional announcement in DataRobot version 10.0](https://docs.datarobot.com/en/docs/release/archive-release-notes/v10.0/v10.0.0-mlops.html#custom-model-training-data-assignment-update)) and [removed in DataRobot version 10.1](https://docs.datarobot.com/en/docs/release/archive-release-notes/v10.1/v10.1.0-mlops.html#custom-model-training-data-assignment-update).

**SaaS:**
To enable feature drift tracking for a model deployment, you must add training data. Previously, you assigned training data directly to a custom model, meaning every version of that model used the same data; however, this assignment method was [deprecated in March 2023](https://docs.datarobot.com/en/docs/release/cloud-history/2023-announce/march2023-announce.html#assign-training-data-to-a-custom-model-version) and [removed in April 2024](https://docs.datarobot.com/en/docs/release/cloud-history/index.html#custom-model-training-data-assignment-update).


On this page, you can review a summary of the conversion process required during the deprecation period from March 2023 to April 2024 and the process for the removed "per model" assignment method:

**Convert model and assign to a model version:**
During the deprecation period from March 2023 to April 2024, assigning training data to a model version required a conversion process:

> [!WARNING] Conversion no longer required
> With the removal of the "per model" assignment method, the conversion step is no longer required. For more information on the current process for assigning training data to a custom model, see the [documentation](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/custom-model-training-data.html).

In
Model Registry
>
Custom Model Workshop
, in the
Models
list, select the model you want to add training data to.
To assign training data to a custom model's versions, you must convert the model. On the
Assemble
tab, locate the
Training data for model versions
alert and click
Permanently convert
:
Training data assignment method conversion
Converting a model's training data assignment method is a one-way action. It
cannot
be reverted. After conversion, you can't assign training data at the model level. This change applies to the UI
and
the API. If your organization has any automation depending on "per model" training data assignment, before you convert a model, you should update any related automation to support the new workflow. As an alternative, you can create a new custom model to convert to the "per version" training data assignment method and maintain the deprecated "per model" method on the model required for the automation; however, you should update your automation before the deprecation process is complete to avoid gaps in functionality.
If the model was already assigned training data, after you convert the model, the
Datasets
section contains information about the existing training dataset.
On the
Assemble
tab, next to
Datasets
:
If the model version
doesn't
have training data assigned, click
Assign
:
If the model version
does
have training data assigned, click the edit icon (
), and, in the
Change Training Data
dialog box, click the delete icon (
) to remove the existing training data.
In the
Add Training Data
(or
Change Training Data
) dialog box, click and drag a training dataset file into the
Training Data
box, or click
Choose file
and do either of the following:
Click
Local file
, select a file from your local storage, and then click
Open
.
Click
AI Catalog
, select a training dataset you previously uploaded to DataRobot, and click
Use this dataset
.
Include features required for scoring
The columns in a custom model's training data indicate which features are included in scoring requests to the deployed custom model; therefore, once training data is available, any features not included in the training dataset aren't sent to the model. This requirement does not apply to predictions made while
testing a custom model
. Available as a preview feature, when you
assemble a custom model
in the NextGen experience, you can disable this behavior using the
Column filtering setting
.
(Optional)
Specify the column name containing partitioning info for your data
(based on training/validation/holdout partitioning). If you plan to deploy the custom model and monitor its
data drift
and
accuracy
, specify the holdout partition in the column to establish an accuracy baseline.
Specify partition column
You can track data drift and accuracy without specifying a partition column; however, in that scenario, DataRobot won't have baseline values. The selected partition column should only include the values
T
,
V
, or
H
.
When the upload is complete, click
Add Training Data
.
Training data assignment error
If the training data assignment fails, an error message appears in the new custom model version under
Datasets
. While this error is active, you can't create a model package to deploy the affected version. To resolve the error and deploy the model package, reassign training data to create a new version, or create a new version and
then
assign training data.

**Assign to a model (removed):**
> [!WARNING] Deprecation notice
> Previously, you assigned training data directly to a custom model, meaning every version of that model uses the same data; however, this assignment method was [deprecated in March 2023](https://docs.datarobot.com/en/docs/release/cloud-history/2023-announce/march2023-announce.html#assign-training-data-to-a-custom-model-version) and [removed in April 2024](https://docs.datarobot.com/en/docs/release/cloud-history/index.html#custom-model-training-data-assignment-update).

This workflow is removed and cannot be used:

In
Model Registry
>
Custom Model Workshop
, in the
Models
list, select the model you want to add training data to.
Click the
Model Info
tab and then click
Add Training Data
(due to the upcoming removal of this method, you should instead prepare to
Permanently convert
the custom model).
The
Add Training Data
dialog box appears, prompting you to upload training data.
Click
Choose file
to upload training data. (Optional) You can specify the column name containing the partitioning information for your data (based on training/validation/holdout partitioning). If you plan to deploy the custom model and monitor its
accuracy
, specify the holdout partition in the column to establish an accuracy baseline. You can still track accuracy without specifying a partition column; however, there will be no accuracy baseline. When the upload is complete, click
Add Training Data
.
Include features required for scoring
The columns in a custom model's training data indicate which features are included in scoring requests to the deployed custom model; therefore, once training data is available, any features not included in the training dataset aren't sent to the model. This requirement does not apply to predictions made while
testing a custom model
. Available as a preview feature, when you
assemble a custom model
in the NextGen experience, you can disable this behavior using the
Column filtering setting
.