Tutorials

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 Fine-tune a pre-trained language model in the IMDB dataset for sentiment classification. Agentic Retrieval Augmented Generation Build an agentic retrieval augmented generation system with ChromaDB and Langchain. HDBSCAN Soft Clustering With Headline Embeddings with GPUs Use HDBSCAN soft clustering with headline embeddings and UMAP on GPUs. Deploy a Fine-Tuned Llama Model to an iOS App with MLC-LLM 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 Securely store Reddit and Slack authentication data while pushing relevant Reddit posts to slack on a consistent basis. Wikipedia Embeddings Generation Create embeddings for the Wikipedia dataset, powered by Union.ai actors. Time Series Forecaster Comparison Visually compare the output of various time series forecasters while maintaining lineage of the training and forecasted data. GluonTS Time Series On GPUs Train and evaluate a time series forecasting model with GluonTS. Credit Default Prediction with XGBoost & NVIDIA RAPIDS Use NVIDIA RAPIDS cuDF DataFrame library and cuML machine learning to predict credit default. Genomic Alignment using Bowtie 2 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 Use open-source models to dub videos. Efficient Named Entity Recognition with vLLM Serve a vLLM model on a warm container and trigger inference automatically with artifacts. Video Generation with Mochi Run the Mochi 1 text-to-video generation model by Genmo on Union.ai. Optimizing the PDF-to-Podcast NVIDIA Blueprint for Production Use Leverage Union.ai to productionize NVIDIA blueprint workflows. Contextual RAG with Together AI Build a contextual RAG workflow for enterprise use. Near-Real-Time Inference with NVIDIA NIM Serve NVIDIA NIM-supported language models, powered by Union.ai actors. Creating a RAG App with LanceDB and Google Gemini Power your RAG app with Union.ai Serving. Taking NVIDIA’s Enterprise RAG Blueprint to Production 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 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 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 Deploy a self-hosted LLM and RAG app with observability and guardrails powered by Weave.