# January 2025 > January 2025 - Read about DataRobot's new features, released in December 2024 and January 2025. 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 `2026-05-06T18:17:10.009019+00:00` (UTC). ## Primary page - [January 2025](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html): Full documentation for this topic (HTML). ## Sections on this page - [December and January features](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#december-and-january-features): In-page section heading. - [GenAI](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#genai): In-page section heading. - [New LLM, Azure OpenAI GPT-4o mini, now available](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#new-llm-azure-openai-gpt-4o-mini-now-available): In-page section heading. - [Modeling](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#modeling): In-page section heading. - [Expanded feature engineering offerings for large datasets](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#expanded-feature-engineering-offerings-for-large-datasets): In-page section heading. - [NextGen model Leaderboard reorganization eases insight navigation](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#nextgen-model-leaderboard-reorganization-eases-insight-navigation): In-page section heading. - [Multilabel modeling available in Workbench](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#multilabel-modeling-available-in-workbench): In-page section heading. - [RAM limit for scoring increased for custom tasks](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#ram-limit-for-scoring-increased-for-custom-tasks): In-page section heading. - [Clustering now supported in Composable ML projects](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#clustering-now-supported-in-composable-ml-projects): In-page section heading. - [Predictions and MLOps](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#predictions-and-mlops): In-page section heading. - [Create and monitor geospatial custom metrics](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#create-and-monitor-geospatial-custom-metrics): In-page section heading. - [Compliance documentation template support for text generation projects](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#compliance-documentation-template-support-for-text-generation-projects): In-page section heading. - [Notebooks](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#notebooks): In-page section heading. - [Debugging now supported in DataRobot Notebooks and codespaces](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/january2025-announce.html#debugging-now-supported-in-datarobot-notebooks-and-codespaces): In-page section heading. ## Related documentation - [AI Platform releases](https://docs.datarobot.com/en/docs/release/index.html): Linked from this page. - [Managed SaaS releases](https://docs.datarobot.com/en/docs/release/cloud-history/index.html): Linked from this page. - [2025 Managed AI Platform releases](https://docs.datarobot.com/en/docs/release/cloud-history/2025-announce/index.html): Linked from this page. - [Self-Managed AI Platform release notes](https://docs.datarobot.com/en/docs/release/archive-release-notes/index.html): Linked from this page. - [LLM availability](https://docs.datarobot.com/en/docs/reference/gen-ai-ref/llm-availability.html): Linked from this page. - [time-aware wrangling](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/dataprep/wrangle-data/build-recipe/ts-wrangling.html): Linked from this page. - [Leaderboard insights](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/index.html#available-insights): Linked from this page. - [multilabel modeling experiments](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/create-experiments/create-predictive/ml-basic-experiment.html#multilabel-targets): Linked from this page. - [Multilabel: Per Label Metrics](https://docs.datarobot.com/en/docs/workbench/nxt-workbench/experiments/experiment-insights/multilabel.html): Linked from this page. - [4GB to 14GB](https://docs.datarobot.com/en/docs/reference/pred-ai-ref/cml-ref/cml-consider.html#task-limit): Linked from this page. - [Composable ML](https://docs.datarobot.com/en/docs/classic-ui/modeling/special-workflows/cml/index.html): Linked from this page. - [create custom metrics](https://docs.datarobot.com/en/docs/workbench/nxt-console/nxt-monitoring/nxt-custom-metrics.html#add-custom-metrics): Linked from this page. - [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): Linked from this page. - [Generate compliance documentation](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-compliance-doc.html): Linked from this page. - [Template Builder for compliance reports](https://docs.datarobot.com/en/docs/classic-ui/modeling/analyze-models/compliance-classic/template-builder.html): Linked from this page. ## Documentation content 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: **Features grouped by capability** | Name | NextGen | Classic | | --- | --- | --- | | GenAI | | | | New LLM, Azure OpenAI GPT-4o mini, now available | ✔ | | | Modeling | | | | Expanded feature engineering offerings for large datasets | ✔ | | | NextGen model Leaderboard reorganization eases insight navigation | ✔ | | | Multilabel modeling available in Workbench | ✔ | ✔ | | RAM limit for scoring increased for custom tasks | ✔ | ✔ | | Clustering now supported in Classic Composable ML projects | ✔ | ✔ | | Predictions and MLOps | | | | Create and monitor geospatial custom metrics | ✔ | | | Compliance documentation template support for text generation projects | ✔ | ✔ | | Notebooks | | | | Debugging now supported in DataRobot Notebooks and codespaces | ✔ | ✔ | ### 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`.