# January 2025
URL: https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html

> Read about DataRobot's new features, released in December 2024 and January 2025.

# December/January SaaS feature announcements

December 2024 and January 2025

This page provides announcements of newly released features in both December 2024 and January 2025, available in DataRobot's SaaS multi-tenant AI Platform, with links to additional resources. From the release center, you can also access past announcements and [Self-Managed AI Platform release notes](https://docs.datarobot.com/en/docs/release/archive-release-notes/index.html).

## December and January features

The following table lists each new feature:

### GenAI

#### New LLM, Azure OpenAI GPT-4o mini, now available

Azure OpenAI’s GPT-4o mini is now generally available for all subscribed enterprise users and Trial users. GPT-4o mini is the most advanced model in the small models category, enabling a broader range of AI applications with its low cost and low latency. GPT-4o mini excels at text and image processing and in the appropriate use cases, should be considered as a replacement for GPT-3.5 Turbo series models. See the full list of [LLM availability](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html) in DataRobot, with links to creator documentation, for assistance in choosing the appropriate model.

### Modeling

#### Expanded feature engineering offerings for large datasets

This deployment brings time-aware predictions with feature transformations to Workbench, allowing you to leverage the benefits of feature engineering with datasets larger than 10GB for time-aware use cases. You can use this methodology in conjunction with [time-aware wrangling](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/build-recipe/ts-wrangling.html) and achieve full transparency of the transformation process. Use the modeling parameters to configure how to assign rows and make predictions based on forecast distance. DataRobot then builds separate models for each distance and makes row-by-row predictions.

#### NextGen model Leaderboard reorganization eases insight navigation

With this deployment, [Leaderboard insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/index.html#available-insights) for both predictive and time-aware experiments are grouped into tabs, with each tab representing the insight's function. Use search to find specific insights as well as to open multiple insights within a tab at once.

Two new insights have been introduced:

- Related Assets, which show which assets are linked to the current model.
- Metric Scores, which provides a single-view listing all partition scores for all metrics.

In addition, four new insights have been ported from DataRobot Classic:

- Logs
- Model info
- Downloads
- Eureqa

#### Multilabel modeling available in Workbench

Predictive modeling now supports multicategorical targets, allowing you to build [multilabel modeling experiments](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-basic-experiment.html#multilabel-targets). Multilabel modeling, a kind of classification task that allows each row in a dataset to be associated with one, several, or zero labels, provides addition flexibility beyond standard multiclass modeling. When setting up the experiment, you can also configure settings that remove selected labels to reduce model complexity. Once modeling completes, use the [Multilabel: Per Label Metrics](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/multilabel.html) insight to evaluate models by summarizing per-label metric performance for metrics across different values of the prediction threshold.

#### RAM limit for scoring increased for custom tasks

Predictions made for models with custom tasks now utilize dynamic memory allocation instead of a fixed memory limit.  The maximum limit has been increased from [4GB to 14GB](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-consider.html#task-limit), supporting experimentation with larger models including deep learning-oriented machine learning experimentation. For increased efficiency, memory allocation for a specific custom task will be determined through testing at fit time.

#### Clustering now supported in Composable ML projects

Clustering, an application of unsupervised learning that lets you explore your data by grouping and identifying natural segments, is now a supported project type for applying [Composable ML](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/index.html) to customize blueprints.

### Predictions and MLOps

#### Create and monitor geospatial custom metrics

When you [create custom metrics](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-custom-metrics.html#add-custom-metrics) and [hosted custom metrics jobs](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-jobs-workshop/nxt-create-jobs/nxt-create-hosted-metric-job.html#add-a-new-hosted-metric-job), you can specify that a metric is geospatial and select a geospatial segment attribute. After you add a geospatial custom metric to a deployment, you can [review the metric data on theCustom metricstab](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-custom-metrics.html#select-chart-type-for-geospatial-metrics), using the new geospatial metric chart view:

#### Compliance documentation template support for text generation projects

With this release, users with template administrator permissions can build compliance documentation templates for text generation projects:

For more information, see [Generate compliance documentation](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-compliance-doc.html) and [Template Builder for compliance reports](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/compliance-classic/template-builder.html).

### Notebooks

#### Debugging now supported in DataRobot Notebooks and codespaces

DataRobot Notebooks now offer built-in support for Python debugger ( [pdb](https://docs.python.org/3/library/pdb.html)) and IPython debugger ( [ipdb](https://pypi.org/project/ipdb/)) to interactively debug your Python code. Choose to activate the debugger before executing code using `ipdb.set_trace()`, or retroactively debug after an exception occurs in the executed code with `%debug` [magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-debug). You can also debug Python scripts in a codespace from the integrated terminal using `pdb`.

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