Tutorials#
This section provides tutorials that walk you through the process of building AI/ML applications on Union. The example 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.
Fine-tunes a Llama 3 model on the Cohere Aya Telugu subset and generates a model artifact for deployment as an iOS app.
Securely store Reddit and Slack authentication data while pushing relevant Reddit posts to slack on a consistent basis.
Create embeddings for the Wikipedia dataset, powered by Union actors.
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.
Use open-source models to dub videos.
Serve a vLLM model on a warm container and trigger inference automatically with artifacts.