Tutorials#
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.
Fine-tune a pre-trained language model in the IMDB dataset for sentiment classification.
Build an agentic retrieval augmented generation system with ChromaDB and Langchain.
Use HDBSCAN soft clustering with headline embeddings and UMAP on GPUs.
Securely store Reddit and Slack authentication data while pushing relevant Reddit posts to slack on a consistent basis.
Visually compare the output of various time series forecasters while maintaining lineage of the training and forecasted data.
Train and evaluate a time series forecasting model with GluonTS.
Use NVIDIA RAPIDS cuDF
DataFrame library and cuML
machine learning to predict credit default.
Pre-process raw sequencing reads, build an index, and perform alignment to the a reference genome using the Bowtie2 aligner.