Overview
Deploying your machine learning app allows others to interact with your models online. Today, you'll learn how to deploy ML apps using platforms like Hugging Face Spaces and Render.
You'll explore deployment workflows, hosting options, and best practices to make your app accessible to the world.
Key Concepts
- Introduction to ML app deployment
- Overview of Hugging Face Spaces for deployment
- Deploying Streamlit or Gradio apps on Hugging Face
- Using Render for app hosting
- Setting up GitHub integration for deployment
- Managing dependencies and environment
- Monitoring and updating deployed apps
Practice Exercise
Exercise: Deploy Your Streamlit ML App
- Create a GitHub repository for your app code.
- Prepare requirements.txt and setup files.
- Deploy the app on Hugging Face Spaces or Render.
- Test the live app URL and share it.
- Try making updates and redeploying.
Resources
Hugging Face Spaces Documentation
Main resource for today
Render Deployment Guide
Step-by-step guide to deploy apps on Render
Deploying Streamlit Apps on Hugging Face
Tutorial for hosting Streamlit apps on Hugging Face Spaces
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