In this part of the docs, you’ll find tutorials that walk you through the process of building AI/ML applications on Union. These applications range from training XGBoost models in tabular datasets to fine-tuning large language models for text generation tasks.

Sentiment Classification with DistilBERT

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

Sentiment Classification with Language Models
Agentic Retrieval Augmented Generation

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

Agentic Retrieval Augmented Generation
Reddit Slack Bot on a Schedule

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

Reddit Slack Bot
Time Series Forecaster Comparison

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

Time Series Model Comparison
Genomic Alignment using Bowtie 2

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