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.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.