# Model building and fine-tuning

> Model building and fine-tuning - Model building and fine-tuning accelerators that you can add to
> your experiment workflow.

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 `2026-05-06T18:17:09.581323+00:00` (UTC).

## Primary page

- [Model building and fine-tuning](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/index.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Feature Discovery workflow](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/afd-e2e.html): Linked from this page.
- [Causal AI for readmission](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/causal-ai.html): Linked from this page.
- [Custom lift charts](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/custom-lift-chart.html): Linked from this page.
- [Fantasy baseball predictions](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/fantasy-baseball.html): Linked from this page.
- [Fine-tune & deploy LLMs](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/finetune-codespace.html): Linked from this page.
- [Hyperparameter optimization](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/hyperopt.html): Linked from this page.
- [Image data with Databricks](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/image-databricks.html): Linked from this page.
- [Production ML with tables](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/ml-tables.html): Linked from this page.
- [Predictions in mobile apps](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/pred-mobile.html): Linked from this page.
- [Order quantity prediction](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/pred-products.html): Linked from this page.
- [Model factory with Python](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/python-multi.html): Linked from this page.
- [Symbolic regression (Eureqa)](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/tune-eureqa.html): Linked from this page.
- [Model marketing attribution](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/model-building-tuning/model-market.html): Linked from this page.

## Documentation content

| Topic | Description |
| --- | --- |
| Feature Discovery workflow | Use a repeatable framework for end-to-end production machine learning. It includes time-aware feature engineering across multiple tables, training dataset creation, model development, and production deployment. |
| Causal AI for readmission | Work with data recording hospital readmission outcomes for diabetes patients to evaluate the causal relationship between the diabetes patients' medication status and their subsequent chance of being readmitted to the hospital. |
| Custom lift charts | Leverage popular Python packages with DataRobot's Python client to recreate and augment the lift chart visualization in DataRobot. |
| Fantasy baseball predictions | Leverage the DataRobot API to quickly build multiple models that work together to predict common fantasy baseball metrics for each player in the upcoming season. |
| Fine-tune & deploy LLMs | Review an end-to-end workflow for fine-tuning and deploying an LLM using features of Hugging Face, Weights and Biases (W&B), and DataRobot. |
| Hyperparameter optimization | Build on the native DataRobot hyperparameter tuning by integrating the hyperopt module into DataRobot workflows. |
| Image data with Databricks | Import image files using Spark and prepare them into a data frame suitable for ingest into DataRobot. |
| Production ML with tables | Explore a repeatable framework for building production ML pipelines that integrate and engineer features from multiple tables. |
| Predictions in mobile apps | Learn how to incorporate DataRobot predictions into a mobile app. |
| Order quantity prediction | Build a model to improve decisions about initial order quantities using future product details and product sketches. |
| Model factory with Python | Learn how to use the Python threading library to build a model factory. |
| Symbolic regression (Eureqa) | Apply symbolic regression to your dataset in the form of the Eureqa algorithm. |
| Model marketing attribution | Use DataRobot to streamline marketing attribution use cases. |