# Tutorials

> **📝 Note**
>
> An LLM-optimized bundle of this entire section is available at [`section.md`](https://www.union.ai/docs/v1/union/tutorials/section.md).
> This single file contains all pages in this section, optimized for AI coding agent context.

This section provides tutorials that walk you through the process of building AI/ML applications on Union.ai.
The example applications range from training XGBoost models in tabular datasets to fine-tuning large language models for text generation tasks.

### [Sentiment Classification with DistilBERT](https://www.union.ai/docs/v1/union/tutorials/language-models/sentiment-classifier/page.md)

Fine-tune a pre-trained language model in the IMDB dataset for sentiment classification.

### [Agentic Retrieval Augmented Generation](https://www.union.ai/docs/v1/union/tutorials/language-models/agentic-rag)

Build an agentic retrieval augmented generation system with ChromaDB and Langchain.

### [HDBSCAN Soft Clustering With Headline Embeddings with GPUs](https://www.union.ai/docs/v1/union/tutorials/language-models/soft-clustering-hdbscan/page.md)

Use HDBSCAN soft clustering with headline embeddings and UMAP on GPUs.

### [Deploy a Fine-Tuned Llama Model to an iOS App with MLC-LLM](https://www.union.ai/docs/v1/union/tutorials/language-models/llama_edge_deployment)

Fine-tune a Llama 3 model on the Cohere Aya Telugu subset and generate a model artifact for deployment as an iOS app.

### [Reddit Slack Bot on a Schedule](https://www.union.ai/docs/v1/union/tutorials/parallel-processing-and-job-scheduling/reddit-slack-bot/page.md)

Securely store Reddit and Slack authentication data while pushing relevant Reddit posts to slack on a consistent basis.

### [ Wikipedia Embeddings Generation](https://www.union.ai/docs/v1/union/tutorials/parallel-processing-and-job-scheduling/wikipedia-embeddings/page.md)

Create embeddings for the Wikipedia dataset, powered by Union.ai actors.

### [Time Series Forecaster Comparison](https://www.union.ai/docs/v1/union/tutorials/time-series/time-series-forecaster-comparison/page.md)

Visually compare the output of various time series forecasters while
maintaining lineage of the training and forecasted data.

### [GluonTS Time Series On GPUs](https://www.union.ai/docs/v1/union/tutorials/time-series/gluonts-time-series/page.md)

Train and evaluate a time series forecasting model with GluonTS.

### [Credit Default Prediction with XGBoost & NVIDIA RAPIDS](https://www.union.ai/docs/v1/union/tutorials/finance/credit-default-xgboost/page.md)

Use NVIDIA RAPIDS `cuDF` DataFrame library and `cuML` machine learning to predict credit default.

### [Genomic Alignment using Bowtie 2](https://www.union.ai/docs/v1/union/tutorials/bioinformatics/alignment/page.md)

Pre-process raw sequencing reads, build an index, and perform alignment to a reference genome using the Bowtie2 aligner.

### [Video Dubbing with Open-Source Models](https://www.union.ai/docs/v1/union/tutorials/multimodal-ai/video-dubbing)

Use open-source models to dub videos.

### [Efficient Named Entity Recognition with vLLM](https://www.union.ai/docs/v1/union/tutorials/language-models/vllm-serving-on-actor)

Serve a vLLM model on a warm container and trigger inference automatically with artifacts.

### [Video Generation with Mochi](https://www.union.ai/docs/v1/union/tutorials/diffusion-models/mochi-video-generation/page.md)

Run the Mochi 1 text-to-video generation model by Genmo on Union.ai.

### [Optimizing the PDF-to-Podcast NVIDIA Blueprint for Production Use](https://www.union.ai/docs/v1/union/tutorials/compound-ai-systems/pdf-to-podcast-blueprint/page.md)

Leverage Union.ai to productionize NVIDIA blueprint workflows.

### [Contextual RAG with Together AI](https://www.union.ai/docs/v1/union/tutorials/retrieval-augmented-generation/contextual-rag/page.md)

Build a contextual RAG workflow for enterprise use.

### [Near-Real-Time Inference with NVIDIA NIM](https://www.union.ai/docs/v1/union/tutorials/serving/nim-on-actor/page.md)

Serve NVIDIA NIM-supported language models, powered by Union.ai actors.

### [Creating a RAG App with LanceDB and Google Gemini](https://www.union.ai/docs/v1/union/tutorials/retrieval-augmented-generation/lance-db-rag/page.md)

Power your RAG app with Union.ai Serving.

### [Taking NVIDIA’s Enterprise RAG Blueprint to Production](https://www.union.ai/docs/v1/union/tutorials/compound-ai-systems/enterprise-rag-blueprint/page.md)

Serve models and run background jobs like data ingestion — all within Union.ai using Union.ai Serving and Union.ai Workflows.

### [Fine-Tune BERT on Arabic Reviews with Multi-Node Training and Data Streaming](https://www.union.ai/docs/v1/union/tutorials/language-models/data-streaming/page.md)

Fine-tune a BERT model on a sizable Arabic review dataset using PyTorch Lightning and the streaming library on a multi-node setup.

### [Trace and Evaluate Models and RAG Apps with Arize](https://www.union.ai/docs/v1/union/tutorials/serving/arize/page.md)

Integrate Arize with your LLMs or RAG applications to trace model activity and evaluate performance in near-real-time.

### [Add Tracing and Guardrails to an Airbnb RAG App with Weave](https://www.union.ai/docs/v1/union/tutorials/serving/weave/page.md)

Deploy a self-hosted LLM and RAG app with observability and guardrails powered by Weave.

## Subpages

- [Bioinformatics](https://www.union.ai/docs/v1/union/tutorials/bioinformatics/page.md)
- [Compound AI Systems](https://www.union.ai/docs/v1/union/tutorials/compound-ai-systems/page.md)
- [Diffusion models](https://www.union.ai/docs/v1/union/tutorials/diffusion-models/page.md)
- [Finance](https://www.union.ai/docs/v1/union/tutorials/finance/page.md)
- [Language Models](https://www.union.ai/docs/v1/union/tutorials/language-models/page.md)
- [Parallel Processing and Job Scheduling](https://www.union.ai/docs/v1/union/tutorials/parallel-processing-and-job-scheduling/page.md)
- [Retrieval Augmented Generation](https://www.union.ai/docs/v1/union/tutorials/retrieval-augmented-generation/page.md)
- [Serving](https://www.union.ai/docs/v1/union/tutorials/serving/page.md)
- [Time Series](https://www.union.ai/docs/v1/union/tutorials/time-series/page.md)

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**Source**: https://github.com/unionai/unionai-docs/blob/main/content/tutorials/_index.md
**HTML**: https://www.union.ai/docs/v1/union/tutorials/