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Documentation for HP AI Studio, a powerful platform for building and deploying machine learning and AI models

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HP AI Studio

Overview

HP AI Studio is a powerful platform designed for building and deploying machine learning and AI models. It offers a robust environment for AI creation, research, experimentation, and prototyping. With HP AI Studio, users can create customized workspaces, containerize tools, and ensure consistency across development environments.

Key Features

  • AI Creation: For creators developing and researching AI models.
  • AI Development: For developers working on AI projects and transferring learning.
  • AI Usage: For users applying AI models for inference and other tasks.

Benefits

  • Standardization: Simplify and standardize the use of GPUs and local compute resources, making it easier to manage multiple devices.
  • Centralized Management: Keep all data, teammates, notebooks, metrics, and artifacts in one place to avoid rework and streamline processes.
  • Project Metadata: Share project metadata to ensure containers can be rebuilt on different machines, with synchronization to GitHub for code management.
  • Accelerated Development: Speed up local model development with a consistent and efficient environment.

Getting Started

  1. Install HP AI Studio: Follow the Installation and Setup guide to get HP AI Studio up and running.
  2. Create a Workspace: Set up a customized workspace for your projects.
  3. Containerize Tools: Learn how to containerize your tools and environments to ensure consistency.
  4. Manage Projects: Use the platform to manage project metadata, sync with GitHub, and avoid rework.

Repository with Data Science experiments compatible with Z by HP Ai Studio

https://github.com/passarel/aistudio-ds-experiments

HP AI Studio overview video step by step guide through the features and capabilitie in the platform, and showcase how to create projects, test, and deploy AI models.

https://community.datascience.hp.com/z-by-hp-ai-studio-early-access-program-73/ai-studio-overview-video-163?postid=243#post243 image

Creating new project to fill out project name,description and add tags.

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Choose predefined workspace or custom workspace to configure the environment.

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Run models and track the machine learning lifecycle and compare their performance.

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Deploy model

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Publish model locally(cloud not available yet)

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MLflow

https://mlflow.org/docs/latest/introduction/index.html#

MLflow Tracking

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Run Comparison Basics

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Model Registry

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🎥 AI Studio: How-To Video Guides

https://community.datascience.hp.com/z-by-hp-ai-studio-early-access-program-73/ai-studio-how-to-video-guides-164?tid=164&fid=73

License

This documentation is licensed under the MIT License. See the LICENSE file for details.

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