- Overview
- Repository Structure
- Data Science and Machine Learning
- Deep Learning
- Generative AI
- NVIDIA GPU Cloud
- Contact and Support
This repository contains a collection of sample projects that you can run quickly and effortlessly, designed to integrate seamlessly with Z by HP AI Studio. Each project runs end-to-end, offering out-of-the-box, ready-to-use solutions across various domains, including data science, machine learning, deep learning, and generative AI.
The projects leverage local open-source models such as LLaMA (Meta), BERT (Google), and CitriNet (NVIDIA), alongside selected online models accessible via Hugging Face. These examples cover a wide range of use cases, including data visualization, stock analysis, audio translation, agentic RAG applications, and much more.
We are continuously expanding this collection with new projects. If you have suggestions or would like to see a specific sample project integrated with Z by HP AI Studio, please feel free to open a new issue in this repository — we welcome your feedback!
- ai-studio-fundamentals
- Iris flowers classification.ipynb
- Recommender Systems.ipynb
- Spam Detection and NLP.ipynb
- [MLFlow] MNIST with Keras.ipynb
- a-tale-of-two-cities-analyzing-trends.ipynb
- deep-learning-in-ais
- bert_qa
- super_resolution
- text_generation
- gen-ai
- agentic_rag_llama
- ngc-integration
- audio_translation_with_nemo_models
- opencellid_eda_with_panel_and_cuDF
- stock_analysis_with_pandas_and_cuDF
- vacation_recommendation_agent_with_bert
The sample projects in this folder demonstrate how to build data science and machine learning applications with Z by HP AI Studio.
We provide 5 sample projects, each designed for quick and easy use to help you get started efficiently.
This project is a simple classification experiment focused on predicting species of Iris flowers.
It runs on the Data Science Workspace, demonstrating basic supervised learning techniques for multi-class classification tasks.
This project performs basic image classification using the TensorFlow framework.
It trains a model to classify handwritten digits from the MNIST dataset and runs on the Deep Learning Workspace.
This project explores a regression experiment using mobility data collected during the COVID-19 pandemic.
It highlights how city-level movement patterns changed during the crisis. The experiment runs on the Data Science Workspace.
This project builds a simple recommender system for movies using TensorFlow.
It trains on user-item interaction data to predict movie preferences and runs on the Deep Learning Workspace.
This project implements a text classification system to detect spam messages.
It uses deep learning techniques and requires the Deep Learning Workspace for training and inference.
The sample projects in this folder demonstrate how to build deep learning applications with Z by HP AI Studio.
We provide 3 sample projects, each designed for quick and easy use to help you get started efficiently.
This project demonstrates a simple BERT Question Answering (QA) experiment. It provides code to train a BERT-based model, as well as instructions to load a pretrained model from Hugging Face.
The model is deployed using MLflow to expose an inference service capable of answering questions based on input text.
This project showcases a Computer Vision experiment that applies convolutional neural networks for image super-resolution — enhancing the quality and resolution of input images.
This project illustrates how to build a simple character-by-character text generation model.
It trains on a dataset containing Shakespeare's texts, demonstrating the fundamentals of text generation by predicting one character at a time.
The sample projects in this folder demonstrate how to build generative AI applications with Z by HP AI Studio.
We provide 1 sample project, each designed for quick and easy use to help you get started efficiently.
This project implements an Agentic Retrieval-Augmented Generation (RAG) pipeline combining Llama 2 and ChromaDB.
It features an intelligent question-answering system where the model dynamically decides whether external document context is needed before responding, ensuring highly accurate and contextually relevant answers through an agentic workflow.
The sample projects in this folder demonstrate how to integrate NVIDIA NGC (NVIDIA GPU Cloud) resources with Z by HP AI Studio.
We provide four distinct sample projects, each designed for quick and easy use to help you get started efficiently.
This project demonstrates an end-to-end audio translation pipeline using NVIDIA NeMo models. It takes an English audio sample and performs:
- Speech-to-Text (STT) conversion using Citrinet
- Text Translation (TT) from English to Spanish using NMT
- Text-to-Speech (TTS) synthesis in Spanish using FastPitch and HiFiGAN
All steps are GPU-accelerated, and the full workflow is integrated with MLflow for experiment tracking and model registration.
This project is a GPU-accelerated, interactive exploratory data analysis (EDA) dashboard for the OpenCellID dataset. It uses Panel and cuDF to deliver lightning-fast geospatial analysis and visualization.
You can explore cell tower distributions by radio type, operator, country, and time window — rendered live on an interactive map with full GPU acceleration.
In this project, we provide notebooks to compare the execution time of dataset operations using traditional Pandas (CPU) versus NVIDIA’s cuDF, a GPU-accelerated drop-in replacement for Pandas. This example is presented in two different formats:
-
Original Example Notebook: This version, created by NVIDIA, runs the entire evaluation within a single notebook. It includes downloading the data and restarting the kernel to activate the cuDF extension.
-
Data Analysis Notebooks: These notebooks use preprocessed datasets of varying sizes from datafabric folder in AI Studio. The evaluation is split across two notebooks—one using Pandas (CPU) and the other using cuDF (GPU)—with performance metrics logged to MLflow.
This project implements an AI-powered recommendation agent that delivers personalized travel suggestions based on user queries.
It leverages the NVIDIA NeMo Framework and BERT embeddings to understand user intent and generate highly relevant, tailored vacation recommendations.
- If you encounter issues, report them via GitHub by opening a new issue.
- Refer to the AI Studio Documentation for detailed guidance and troubleshooting.
Built with ❤️ using Z by HP AI Studio.