🧪Spaces

NEW

Also known as: Hugging Face Spaces

A hosted platform from Hugging Face for deploying machine learning demos and apps with simple workflows.

DESCRIPTION

Hugging Face Spaces lets you host ML demos using frameworks like Gradio and Streamlit directly from a repository. It builds and serves apps automatically, handling dependencies and updates. Spaces support private or public sharing, GPU acceleration, and secrets for API keys. Teams use it to showcase models, gather feedback, and iterate quickly. It integrates with the Hugging Face Hub for model and dataset artifacts, enabling reproducible demos and interactive documentation.

ALTERNATIVE DEFINITIONS

Hugging Face Docs

Spaces are a simple way to host your machine learning apps on Hugging Face.

Stresses simplicity and hosted app model.

Source

Hugging Face

Deploy machine learning demos and apps in minutes with Spaces using frameworks like Gradio or Streamlit.

Highlights fast deployment and supported frameworks.

Source

RELATED TERMS

Gradio Streamlit Model Demo Inference Endpoint FastAPI Docker CI-CD Model Cards Hugging Face Hub

COMPETING TERMS

Streamlit Community Cloud Replicate

RELATIONS

• Narrower: Common European Data Spaces

KNOWLEDGE AREAS

Platform & Tooling AI Data Products Data Visualization

TARGET AUDIENCE

AI Engineer Data Scientist Software Developer MLOps Engineer UI-UX Designer

EXTERNAL SOURCES

• Hugging Face - Official Spaces documentation
• Streamlit - Comparable app hosting platform

TOP QUESTIONS

What can I build with Spaces?

Interactive ML demos, prototypes, and small apps using Gradio or Streamlit. The statement: it’s ideal for showcasing models quickly.

Category: UseCases

How does Spaces compare to Streamlit Community Cloud?

Spaces integrates tightly with the Hugging Face Hub and supports GPU-backed apps. Streamlit Cloud focuses on general Python apps and sharing.

Category: Comparison

Can I make a private Space?

Yes, private Spaces restrict access to collaborators or specific users. This is useful before public launches.

Category: Features

How do I handle secrets in Spaces?

Use the built-in Secrets feature to store API keys securely. Never hardcode secrets in code.

Category: Security

Does Spaces support GPUs?

Yes, certain tiers provide GPU hardware for inference-heavy demos. Choose the right hardware for model size.

Category: Performance

What are common mistakes when deploying to Spaces?

Incorrect dependencies in requirements.txt and large model downloads on startup. Cache artifacts and test locally.

Category: Mistakes

How does CI/CD work with Spaces?

Pushes to the repository trigger rebuilds automatically. The statement: treat the Space like a production repo.

Category: HowTo

Can Spaces scale to heavy traffic?

It can handle significant traffic but is best for demos and mid-scale apps. For massive scale, consider dedicated hosting.

Category: Advice

How do I collect user feedback on Spaces?

Add logging, analytics, or forms; link to issue trackers. Feedback loops accelerate iterative improvement.

Category: Operations