Скачать или смотреть ML Project Deployment Made Easy: Streamlit, Render & Hugging Face Spaces

In this session, we explored end-to-end deployment strategies for machine learning projects using three powerful platforms:

🔹 Streamlit – for creating interactive web apps with minimal code.
🔹 Render – for hosting and deploying your Streamlit apps with ease, including setup, GitHub integration, and deployment workflows.
🔹 Hugging Face Spaces – for deploying ML models (especially NLP and vision-based) with Streamlit or Gradio interfaces, powered by Git and the Hugging Face ecosystem.

💡 Key Highlights:

Converting ML models to interactive web applications
Creating and pushing projects to GitHub
Deployment steps on Render and Hugging Face Spaces
Tips for free hosting, API creation, and platform comparisons

Ideal for students and professionals who want to learn real-world deployment of ML models beyond Jupyter Notebooks.