---
type: concept
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  pageDirectory: concepts
  sources:
    - ai-runtime-example-notebooks-databricks-on-aws.md
  confidence: 0.95
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  freshnessStatus: unverified
  aliases:
    - ai-runtime-example-notebooks
    - AREN
  citations:
    - file: ai-runtime-example-notebooks-databricks-on-aws.md
title: AI Runtime example notebooks
description: Curated collection of notebook examples demonstrating AI Runtime usage for LLMs, computer vision, recommender systems, classic ML, and distributed training.
tags:
  - notebooks
  - examples
  - documentation
timestamp: "2026-06-19T17:31:49.929Z"
---

# AI Runtime Example Notebooks

**AI Runtime Example Notebooks** are reference notebooks that demonstrate how to use [AI Runtime](/concepts/ai-runtime.md) for various machine learning tasks. The notebooks cover a range of domains and training scales, from single‑node workloads to multi‑GPU distributed training. ^[ai-runtime-example-notebooks-databricks-on-aws.md]

AI Runtime for single‑node tasks is in **Public Preview**, while the distributed training API for multi‑GPU workloads remains in **Beta**. ^[ai-runtime-example-notebooks-databricks-on-aws.md]

## Notebook Categories

The example notebooks are organized into five categories: Large Language Models, Computer Vision, Deep Learning Based Recommender Systems, Classic ML, and Multi‑GPU Distributed Training. ^[ai-runtime-example-notebooks-databricks-on-aws.md]

- **Large Language Models (LLMs)** — Examples for fine‑tuning large language models, including parameter‑efficient methods. See [Large Language Models (LLMs)](/concepts/large-language-models-llms-on-databricks.md) and [Parameter-Efficient Fine-Tuning](/concepts/parameter-efficient-fine-tuning-peft.md).
- **Computer Vision** — Examples for tasks such as object detection and image classification. See [Computer Vision](/concepts/computer-vision-on-databricks.md).
- **Deep Learning Based Recommender Systems** — Examples for building modern recommendation systems using approaches like two‑tower models. See Recommender Systems.
- **Classic ML** — Examples for traditional machine learning tasks, including [XGBoost](/concepts/xgboostspark-module.md) model training and [Time Series Forecasting](/concepts/multi-series-forecasting.md).
- **Multi‑GPU Distributed Training** — Examples for scaling training across multiple GPUs and nodes using the Serverless GPU API. See [Multi-GPU Distributed Training](/concepts/multi-gpu-distributed-training-api.md) and [Serverless GPU API](/concepts/serverless-gpu-api.md).

## Accessing the Notebooks

Each category links to a dedicated page that contains the actual notebook examples. Those pages provide step‑by‑step code and explanations for applying AI Runtime to the respective task. ^[ai-runtime-example-notebooks-databricks-on-aws.md]

## Related Concepts

- [AI Runtime](/concepts/ai-runtime.md)
- [Large Language Models (LLMs)](/concepts/large-language-models-llms-on-databricks.md)
- [Computer Vision](/concepts/computer-vision-on-databricks.md)
- Recommender Systems
- [XGBoost](/concepts/xgboostspark-module.md)
- [Time Series Forecasting](/concepts/multi-series-forecasting.md)
- [Multi-GPU Distributed Training](/concepts/multi-gpu-distributed-training-api.md)
- [Serverless GPU API](/concepts/serverless-gpu-api.md)
- [Parameter-Efficient Fine-Tuning](/concepts/parameter-efficient-fine-tuning-peft.md)

## Sources

- ai-runtime-example-notebooks-databricks-on-aws.md

# Citations

1. [ai-runtime-example-notebooks-databricks-on-aws.md](/references/ai-runtime-example-notebooks-databricks-on-aws-09849715.md)
