---
title: July 2023
description: Read about DataRobot's new public preview and generally available features released in July, 2023.

---

# July 2023 {: #july-2023 }

_July 26, 2023_

With the latest deployment, DataRobot's AI Platform delivered the new GA and Public Preview features listed below. From the release center you can also access:

* [Monthly deployment announcement history](cloud-history/index)
* [Public preview features](public-preview/index)
* [Self-Managed AI Platform release notes](archive-release-notes/index)


## July release {: #july-release }

The following table lists each new feature:

??? abstract "Features grouped by capability"

    Name       |  GA | Public Preview
    ---------- | ---- | ---
    **Applications** |  :~~:  | :~~:
    [Improvements to the new app experience in Workbench](#improvements-to-the-new-app-experience-in-workbench) |  | ✔
    **Data** |  :~~:  | :~~:
    [BigQuery support added to Workbench](#bigquery-support-added-to-workbench)  |  | ✔
    [Materialize wrangled datasets in Snowflake](#materialize-wrangled-datasets-in-snowflake)  |  | ✔
    [Perform joins and aggregations on your data in Workbench](#perform-joins-and-aggregations-on-your-data-in-workbench) |  | ✔
    [Publish recipes with smart downsampling](#publish-recipes-with-smart-downsampling) |  | ✔
    [Improvements to data preparation in Workbench](#improvements-to-data-preparation-in-workbench) |  | ✔
    [BigQuery connection enhancements](#bigquery-connection-enhancements) |  | ✔
	  **Modeling** |  :~~:  | :~~:
    [Tune hyperparameters for custom tasks](#tune-hyperparameters-for-custom-tasks) |  | ✔ |
    [Sklearn library upgrades](#sklearn-library-upgrades) | ✔ |  |
    **Predictions and MLOps** |  :~~:  | :~~:
    [DataRobot provider for Apache Airflow](#datarobot-provider-for-apache-airflow) | ✔ |
    [MLflow integration for the Model Registry](#mlflow-integration-for-the-datarobot-model-registry) |   | ✔ |
    [Monitoring jobs for custom metrics](#monitoring-jobs-for-custom-metrics) |   | ✔ |
    [Timeliness indicators for predictions and actuals](#timeliness-indicators-for-predictions-and-actuals) |   | ✔ |
    [Versioning support in the Model Registry](#versioning-support-in-the-model-registry) |   | ✔ |
    [Public network access for custom models](#public-network-access-for-custom-models) |   | ✔ |
    [Monitoring support for generative models](#monitoring-support-for-generative-models) |   | ✔ |
    **API enhancements** |  :~~:  | :~~:
    [DataRobot REST API v2.31](#datarobot-rest-api-v231) | ✔  |  |
    [R client v2.31](#r-client-v231) | | ✔ |
    [R client v2.18.3](#r-client-v2183) | ✔ | |

## Data enhancements {: #data-enhancements }

### Public Preview {: #public-preview }

#### BigQuery support added to Workbench {: #bigquery-support-added-to-workbench }

Support for Google BigQuery has been added to Workbench, allowing you to:

- [Create and configure data connections.](wb-connect)
- [Add BigQuery datasets to a Use Case.](wb-connect#select-a-dataset)
- [Wrangle BigQuery datasets](wb-add-operation), and then [publish recipes to BigQuery](wb-pub-recipe) to materialize the output in the Data Registry.

**Feature flag:** Enable Native BigQuery Driver

#### Materialize wrangled datasets in Snowflake {: #materialize-wrangled-datasets-in-snowflake }

You can now publish wrangling recipes to materialize data in DataRobot’s Data Registry or Snowflake. When you publish a wrangling recipe, operations are pushed down into a Snowflake virtual warehouse, allowing you to leverage the security, compliance, and financial controls of Snowflake. By default, the output dataset is materialized in DataRobot's Data Registry. Now you can materialize the wrangled dataset in Snowflake databases and schemas for which you have write access.

![](images/wb-snow-mat-2.png)

Public preview [documentation](wb-pub-recipe#publish-to-Snowflake).

**Feature flags:** Enable Snowflake In-Source Materialization in Workbench, Enable Dynamic Datasets in Workbench

#### Perform joins and aggregations on your data in Workbench {: #perform-joins-and-aggregations-on-your-data-in-workbench }

You can now add **Join** and **Aggregation** operations to your wrangling recipe in Workbench. Use the Join operation to combine datasets that are accessible via the same connection instance, and the Aggregation operation to apply aggregation functions like sum, average, counting, minimum/maximum values, standard deviation, and estimation, as well as some non-mathematical operations to features in your dataset.

Public preview [documentation](wb-add-operation).

**Feature flag:** Enable Additional Wrangler Operations

#### Publish recipes with smart downsampling {: #publish-recipes-with-smart-downsampling }

When [publishing a wrangling recipe](wb-pub-recipe) in Workbench, use smart downsampling to reduce the size of your output dataset and optimize model training. Smart downsampling is a data science technique to reduce the time it takes to fit a model without sacrificing accuracy. This downsampling technique accounts for class imbalance by stratifying the sample by class. In most cases, the entire minority class is preserved and sampling only applies to the majority class, which is particularly useful for imbalanced data. Because accuracy is typically more important on the minority class, this technique greatly reduces the size of the training dataset, reducing modeling time and cost while preserving model accuracy.

![](images/wb-smart-down-1.png)

**Feature flag:** Enable Smart Downsampling in Wrangle Publishing Settings

#### Improvements to data preparation in Workbench {: #improvements-to-data-preparation-in-workbench }

This release introduces several improvements to the data preparation experience in Workbench.

Workbench now supports _dynamic datasets_.

- Datasets added via a data connection will be registered as dynamic datasets in the Data Registry and Use Case.
- Dynamic datasets added via a connection will be available for selection in the Data Registry.
- DataRobot will pull a new live sample when viewing Exploratory Data Insights for dynamic datasets.

**Feature flag:** Enable Dynamic Datasets in Workbench

You can now _view and create custom feature lists_ while exploring datasets registered in a Workbench Use Case.

![](images/wb-ft-list-1.png)

Public preview [documentation](wb-data-tab#feature-lists).

**Feature flag:** Enable Feature Lists in Workbench Preview

Additionally, for wrangled datasets added to a Use Case, you can now _view the SQL recipe_ used to generate the output.

![](images/rn-view-sql.png)

#### BigQuery connection enhancements {: #bigquery-connection-enhancements }

A new BigQuery connector is now available for public preview, providing several performance and compatibility enhancements, as well as support for authentication using Service Account credentials.

Public preview [documentation](dc-bigquery).

**Feature flag:** Enable Native BigQuery Driver

## Modeling enhancements {: #modeling-enhancements }

### GA {: #ga }

#### Sklearn library upgrades {: #sklearn-library-upgrades}

In this release, the sklearn library was upgraded from 0.15.1 to 0.24.2. The impacts are summarized as follows:

* Feature association insights: Updated the spectral clustering logic. This only affects the cluster ID (a numeric identifier for each cluster, e.g., 0, 1, 2, 3). The values of feature association insights are not affected.

* AUC/ROC insights: Due to the improvement in sklearn ROC curve calculation, the precision of AUC/ROC values are slightly affected.

### Public Preview {: #public-preview }

#### Tune hyperparameters for custom tasks {: #tune-hyperparameters-for-custom-tasks }

You can now tune hyperparameters for custom tasks. You can provide two values for each hyperparameter: the `name` and `type`. The type can be one of `int`, `float`, `string`, `select`, or `multi`, and all types support a `default` value.  See [Model metadata and validation schema](cml-validation) for more details and example configuration of hyperparameters.

Public preview [documentation](cml-hyperparam#configure-hyperparameters-for-custom-tasks).

## No-Code AI App enhancements {: #no-code-ai-app-enhancements }

### Public Preview {: #public-preview }

#### Improvements to the new app experience in Workbench {: #improvements-to-the-new-app-experience-in-workbench }

This release introduces the following improvements to the new application experience (available for public preview) in Workbench:

- The **Overview** folder now displays the blueprint of the model used to create the application.
- Alpine Light has been added to the available app themes.

Public preview [documentation](wb-app-edit).

**Feature flag:** Enable New No-Code AI Apps Edit Mode

## MLOps enhancements {: #mlops-enhancements }

### GA {: #ga }

#### DataRobot provider for Apache Airflow {: #datarobot-provider-for-apache-airflow  }

Now generally available, you can combine the capabilities of [DataRobot MLOps](mlops/index) and [Apache Airflow](https://airflow.apache.org/docs/){ target=_blank } to implement a reliable solution for retraining and redeploying your models; for example, you can retrain and redeploy your models on a schedule, on model performance degradation, or using a sensor that triggers the pipeline in the presence of new data. The DataRobot provider for Apache Airflow is a Python package built from [source code available in a public GitHub repository](https://github.com/datarobot/airflow-provider-datarobot){ target=_blank } and [published in PyPi (The Python Package Index)](https://pypi.org/project/airflow-provider-datarobot/){ target=_blank }. It is also [listed in the Astronomer Registry](https://registry.astronomer.io/providers/datarobot/versions/latest){ target=_blank }. The integration uses [the DataRobot Python API Client](https://pypi.org/project/datarobot/){ target=_blank }, which communicates with DataRobot instances via REST API.

![](images/airflow-datarobot-pipeline-dag.png)

For more information, see the [DataRobot provider for Apache Airflow](apache-airflow) quickstart guide.

### Public Preview {: #public-preview }

#### MLflow integration for the DataRobot Model Registry {: #mlflow-integration-for-the-datarobot-model-registry }

The public preview release of the MLflow integration for DataRobot allows you to export a model from MLflow and import it into the DataRobot [Model Registry](registry/index), creating [key values](reg-key-values) from the training parameters, metrics, tags, and artifacts in the MLflow model. You can use the integration's command line interface to carry out the export and import processes:

```sh title="Import from MLflow"
DR_MODEL_ID="<MODEL_PACKAGE_ID>"

env PYTHONPATH=./ \
python datarobot_mlflow/drflow_cli.py \
  --mlflow-url http://localhost:8080 \
  --mlflow-model cost-model  \
  --mlflow-model-version 2 \
  --dr-model $DR_MODEL_ID \
  --dr-url https://app.datarobot.com \
  --with-artifacts \
  --verbose \
  --action sync
```

Public preview [documentation](mlflow-integration).

**Feature flag:** Enable Extended Compliance Documentation

#### Monitoring jobs for custom metrics {: #monitoring-jobs-for-custom-metrics }

Now available for public preview, monitoring job definitions allow DataRobot to pull calculated custom metric values from outside of DataRobot into the custom metric defined on the [Custom Metrics](custom-metrics) tab, supporting custom metrics with external data sources. For example, you can create a monitoring job to connect to Snowflake, fetch custom metric data from the relevant Snowflake table, and send the data to DataRobot:

![](images/monitoring-options-custom-metrics.png)

Public preview [documentation](custom-metric-monitoring-jobs).

**Feature flag:** Enable Custom Metrics Job Definitions

#### Timeliness indicators for predictions and actuals {: #timeliness-indicators-for-predictions-and-actuals }

Deployments have several statuses to define the general health of a deployment, including [Service Health](service-health), [Data Drift](data-drift), and [Accuracy](deploy-accuracy). These statuses are calculated based on the most recent available data. For deployments relying on batch predictions made in intervals greater than 24 hours, this method can result in an unknown status value on the [Prediction Health indicators in the deployment inventory](deploy-inventory#prediction-health-lens). Now available for Public Preview, those deployment health indicators can retain the most recently calculated health status, presented along with _timeliness_ status indicators to reveal when they are based on old data. You can determine the appropriate timeliness intervals for your deployments on a case-by-case basis. Once you've enabled timeliness tracking on a deployment's **Usage > Settings** tab, you can view timeliness indicators on the [**Usage** tab](deploy-usage) and in the [**Deployments** inventory](deploy-inventory):

=== "Deployments inventory"

    View the **Predictions Timeliness** and **Actuals Timeliness** columns:

    ![](images/timeliness-columns.png)

=== "Usage tab"

    View the **Predictions Timeliness** and **Actuals Timeliness** tiles:

    ![](images/timeliness-tiles.png)

    Along with the status, you can view the **Updated** time for each timeliness tile.

!!! note
     In addition to the indicators on the **Usage** tab and the **Deployments** inventory, when a timeliness status changes to _Red / Failing_, a notification is sent through email or the [channel configured in your notification policies](web-notify).

Public preview [documentation](timeliness-status-indicators).

**Feature flag:** Enable Timeliness Stats Indicator for Deployments

#### Versioning support in the Model Registry {: #versioning-support-in-the-model-registry }

The Model Registry is an organizational hub for various models used in DataRobot, where you can access models as deployment-ready model packages. Now available as a public preview feature, the **Model Registry > Registered Models** page provides an additional layer of organization to your models.

![](images/reg-models-page.png)

On this page, you can group model packages into _registered models_, allowing you to categorize them based on the business problem they solve. Registered models can contain:

* DataRobot, custom, and external models

* Challenger models (alongside the champion)

* Automatically retrained models.

Once you add registered models, you can search, filter, and sort them. You can also share your registered models (and the versions they contain) with other users.

For more information, see the [Model Registry](registry/index) documentation.

**Feature flag:** Enable Versioning Support in the Model Registry

#### Public network access for custom models {: #public-network-access-for-custom-models }

Now available as a public preview feature, you can enable full network access for any custom model. When you create a custom model, you can access any fully qualified domain name (FQDN) in a public network so that the model can leverage third-party services. Alternatively, you can disable public network access if you want to isolate a model from the network and block outgoing traffic to enhance the security of the model. To review this access setting for your custom models, on the **Assemble** tab, under **Resource Settings**, check the **Network access**:

![](images/network-access-setting.png)

For more information, see the [documentation](custom-model-resource-mgmt).

**Feature flag:** Enable Public Network Access for all Custom Models

#### Monitoring support for generative models {: #monitoring-support-for-generative-models }

Now available as a Public Preview feature, the text generation target type for DataRobot custom and external models is compatible with generative Large Language Models (LLMs), allowing you to deploy generative models, make predictions, monitor service, usage, and data drift statistics, and create custom metrics. DataRobot supports LLMs through two deployment methods:

* [Create a text generation model as a custom inference model in DataRobot](generative-model-monitoring#create-and-deploy-a-generative-custom-inference-model): Create and deploy a text generation model using DataRobot's Custom Model Workshop, calling the LLM's API to generate text instead of performing inference directly and allowing DataRobot MLOps to access the LLM's input and output for monitoring. To call the LLM's API, you should [enable public network access for custom models](custom-model-resource-mgmt).

* [Monitor a text generation model running externally](generative-model-monitoring#create-and-deploy-an-external-generative-model): Create and deploy a text generation model on your infrastructure (local or cloud), using the monitoring agent to communicate the input and output of your LLM to DataRobot for monitoring.

After you deploy a generative model, you can view [service health](service-health) and [usage](deploy-usage) statistics, export [deployment data](data-export), create [custom metrics](custom-metrics), and identify [data drift](data-drift). On the **Data Drift** tab for a generative model, you can view the [**Feature Drift vs. Feature Importance**](data-drift#feature-drift-vs-feature-importance-chart), [**Feature Details**](generative-model-monitoring#feature-details-for-generative-models), and [**Drift Over Time**](data-drift#drift-over-time-chart) charts.

=== "Data Drift"

    ![](images/text-generation-data-drift.png)

=== "Service Health"

    ![](images/text-generation-service-health.png)

=== "Usage"

    ![](images/text-generation-usage.png)

=== "Data Export"

    ![](images/text-generation-data-export.png)

=== "Custom Metrics"

    ![](images/text-generation-custom-metrics.png)


Public preview [documentation](generative-model-monitoring).

**Feature flags:** [Enable Monitoring Support for Generative Models](generative-model-monitoring), [Enable the Injection of Runtime Parameters for Custom Models](pp-cus-model-runtime-params)

## API enhancements {: #api-enhancements }

### DataRobot REST API v2.31 {: #datarobot-rest-api-v231 }

#### New features {: #new-features }

- New route to retrieve deployment fairness score over time:
  - `GET /api/v2/deployments/(deploymentId)/fairnessScoresOverTime/`
- New route to retrieve deployment predictions stats over time:
  - `GET /api/v2/deployments/(deploymentId)/predictionsOverTime/`
- New routes to calculate and retrieve sliced insights:
  - `POST /api/v2/insights/featureEffects/`
  - `GET /api/v2/insights/featureEffects/models/(entityId)/`
  - `POST /api/v2/insights/featureImpact/`
  - `GET /api/v2/insights/featureImpact/models/(entityId)/`
  - `POST /api/v2/insights/liftChart/`
  - `GET /api/v2/insights/liftChart/models/(entityId)/`
  - `POST /api/v2/insights/residuals/`
  - `GET /api/v2/insights/residuals/models/(entityId)/`
  - ` POST/api/v2/insights/rocCurve/`
  - `GET GET /api/v2/insights/rocCurve/models/(entityId)/`
- New routes to create and manage data slices for use with sliced insights:
  - `POST /api/v2/dataSlices/`
  - :http:delete:`/api/v2/dataSlices/`
  - `DELETE /api/v2/dataSlices/(dataSliceId)/`
  - `GET /api/v2/dataSlices/(dataSliceId)/`
  - `GET /api/v2/projects/(projectId)/dataSlices/`
  - `POST /api/v2/dataSlices/(dataSliceId)/sliceSizes/`
  - `GET /api/v2/dataSlices/(dataSliceId)/sliceSizes/`
- New route to register a Leaderboard model:
  - `POST /api/v2/modelPackages/fromLeaderboard/`
- New routes to create and manage Value Trackers (former Use Cases):
  - `POST /api/v2/valueTrackers/`
  - `GET /api/v2/valueTrackers/`
  - `GET /api/v2/valueTrackers/(valueTrackerId)/`
  - `PATCH /api/v2/valueTrackers/(valueTrackerId)/`
  - `DELETE /api/v2/valueTrackers/(valueTrackerId)/`
  - `GET /api/v2/valueTrackers/(valueTrackerId)/activities/`
  - `GET /api/v2/valueTrackers/(valueTrackerId)/attachments/`
  - `POST /api/v2/valueTrackers/(valueTrackerId)/attachments/`
  - `DELETE /api/v2/valueTrackers/(valueTrackerId)/attachments/(attachmentId)/`
  - `GET /api/v2/valueTrackers/(valueTrackerId)/attachments/(attachmentId)/`
  - `GET /api/v2/valueTrackers/(valueTrackerId)/realizedValueOverTime/`
  - `GET /api/v2/valueTrackers/(valueTrackerId)/sharedRoles/`
  - `PATCH /api/v2/valueTrackers/(valueTrackerId)/sharedRoles/`

#### API changes {: #api-changes }

- Added training and holdout data assignment to the custom model version creation endpoints:
  - `POST /api/v2/customModels/(customModelId)/versions/`
  - `PATCH /api/v2/customModels/(customModelId)/versions/`


- The Organization Administrator route for removing users from an organization, `DELETE /api/v2/organizations/(organizationId)/users/(userId)/` has been removed. Instead, they should be deactivated, or a system administrator can move the user to a different organization.

- Adds the ``useGpu`` option/parameter. When GPU workers are enabled, this option controls whether the project should use GPU workers. The parameter is added to the following route:
    - `PATCH /api/v2/projects/(projectId)/aim/`

- The ``useGpu`` option/parameter will also be returned as a new field when project data is retrieved using route:
  - `GET /api/v2/projects/(projectId)/`

- The new optional parameters ``modelBaselines``, ``modelRegimeId``, ``modelGroupId`` for OTV Time Series projects without FEAR are added to: `PATCH /api/v2/projects/(projectId)/aim/`. To use these fields, enable the feature flag `Forecasting Without Automated Feature Derivation`.

#### Deprecations {: #deprecations }

- The following custom inference models training data assignment endpoints are deprecated and will be removed in version 2.33:
  - `PATCH /api/v2/customModels/(customModelId)/trainingData/`
  - `PATCH /api/v2/customModels/(customModelId)/versions/withTrainingData/`

- The following route to register a Leaderboard model is deprecated in favor of `POST /api/v2/modelPackages/fromLeaderboard/` and will be removed in v2.33:
  - `POST /api/v2/modelPackages/fromLearningModel/`

- The following use case manage endpoints are deprecated in favor of new `GET /api/v2/valueTrackers/` based endpoints and will be removed in v2.33:
    - `POST /api/v2/useCases/`
    - `GET /api/v2/useCases/`
    - `GET /api/v2/useCases/(useCaseId)/`
    - `PATCH /api/v2/useCases/(useCaseId)/`
    - `DELETE /api/v2/useCases/(useCaseId)/`
    - `GET /api/v2/useCases/(useCaseId)/activities/`
    - `GET /api/v2/useCases/(useCaseId)/attachments/`
    - `POST /api/v2/useCases/(useCaseId)/attachments/`
    - `DELETE /api/v2/useCases/(useCaseId)/attachments/(attachmentId)/`
    - `GET /api/v2/useCases/(useCaseId)/attachments/(attachmentId)/`
    - `GET /api/v2/useCases/(useCaseId)/realizedValueOverTime/`
    - `GET /api/v2/useCases/(useCaseId)/sharedRoles/`
    - `PATCH /api/v2/useCases/(useCaseId)/sharedRoles/`

- Current `useCases/` endpoints are being renamed to `valueTracker/` endpoints. Current `useCases/` endpoints will sunset in two releases, API 2.33. In place of the current `useCases/` endpoints, please begin using the `valueTrackers/` endpoints.

### R client v2.31 {: #r-client-v231 }

Version v2.31 of the R client is now available for public preview. It can be installed via [GitHub](https://github.com/datarobot/rsdk/blob/main/datarobot/NEWS.md#datarobot-v23109000).

This version of the R client addresses an issue where a new feature in the `curl==5.0.1` package caused any invocation of `datarobot:::UploadData` (i.e., `SetupProject`) to fail with the error `No method asJSON S3 class: form_file`.

#### Enhancements {: #enhancements }

The unexported function `datarobot:::UploadData` now takes an optional argument `fileName`.

#### Bugfixes {: #bugfixes }

Loading the `datarobot` package with `suppressPackageStartupMessages()` will now suppress all messages.

#### Deprecations {: #deprecations }

* `CreateProjectsDatetimeModelsFeatureFit` has been removed. Use `CreateProjectsDatetimeModelsFeatureEffects` instead.
* `ListProjectsDatetimeModelsFeatureFit` has been removed. Use `ListProjectsDatetimeModelsFeatureEffects` instead.
* `ListProjectsDatetimeModelsFeatureFitMetadata` has been removed. Use `ListProjectsDatetimeModelsFeatureEffectsMetadata` instead.
* `CreateProjectsModelsFeatureFit` has been removed. Use CreateProjectsModelsFeatureEffects instead.
* `ListProjectsModelsFeatureFit` has been removed. Use `ListProjectsModelsFeatureEffects` instead.
* `ListProjectsModelsFeatureFitMetadata` has been removed. Use `ListProjectsModelsFeatureEffectsMetadata` instead.

#### Dependency changes {: #dependency-changes }

Client documentation is now explicitly generated with Roxygen2 v7.2.3.
Added Suggests: mockery to improve unit test development experience.

### R client v2.18.3 {: #r-client-v2183 }

Version v2.31 of the R client is now generally available. It can be accessed via [CRAN](https://cran.r-project.org/web/packages/datarobot/index.html).

The `datarobot` package is now dependent on R >= 3.5.

#### New features {: #new-features }

* The R client will now output a warning when you attempt to access certain resources (projects, models, deployments, etc.) that are deprecated or disabled by the DataRobot platform migration to Python 3.

* Added support for comprehensive autopilot: use `mode = AutopilotMode.Comprehensive`.

#### Enhancements {: #enhancements }

* The function `RequestFeatureImpact` now accepts a `rowCount` argument, which will change the sample size used for Feature Impact calculations.

* The un-exported function `datarobot:::UploadData` now takes an optional argument `fileName`.

#### Bugfixes {: #bugfixes }

* Fixed an issue where an undocumented feature in `curl==5.0.1` is installed that caused any invocation of `datarobot:::UploadData` (i.e., `SetupProject`) to fail with the error `No method asJSON S3 class: form_file`.

* Loading the `datarobot` package with `suppressPackageStartupMessages()` will now suppress all messages.

#### API changes {: #api-changes }

* The functions `ListProjects` and `as.data.frame.projectSummaryList` no longer return fields related to recommender models, which were removed in v2.5.0.

* The function `SetTarget` now sets autopilot mode to Quick by default. Additionally, when Quick is passed, the underlying `/aim` endpoint will no longer be invoked with Auto.

#### Deprecations {: #deprecations }

* The `quickrun` argument is removed from the function `SetTarget`. Users should set `mode = AutopilotMode.Quick` instead.

* Compliance Documentation was deprecated in favor of the Automated Documentation API.

#### Dependency changes {: #dependency-changes }

* The `datarobot` package is now dependent on R >= 3.5 due to changes in the updated "Introduction to DataRobot" vignette.

* Added dependency on `AmesHousing` package for updated "Introduction to DataRobot" vignette.

* Removed dependency on `MASS` package.

* Client documentation is now explicitly generated with Roxygen2 v7.2.3.

#### Documentation changes {: #documentation-changes }

* Updated the "Introduction to DataRobot" vignette to use Ames, Iowa housing data instead of the Boston housing dataset.

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