# My Experience Choosing Deployment Platforms for ML Projects > Source: > Published: 2026-08-10 13:56:37+00:00 When deploying a Machine Learning project, choosing the right platform for the frontend, backend, and source code can make the process much easier. Here are some popular options I explored: 🎨 Frontend Deployment Vercel is one of my preferred choices for frontend deployment, especially for modern web applications. Why I like it: Easy deployment GitHub integration Automatic deployments Good performance Simple configuration ⚙️ Backend Deployment Hugging Face Spaces Useful for Machine Learning and AI applications Supports ML-focused deployments Can be convenient for demos and prototypes Free and paid options are available depending on the service and requirements Render Simple deployment process Supports backend applications Provides a free tier with limitations Suitable for smaller projects and prototypes Railway Easy backend deployment Supports databases and backend services Convenient for applications that need more resources Useful for ML-backed APIs depending on the model size and resource requirements 💻 Source Code For managing and sharing source code, I prefer GitHub. It helps with: Version control Collaboration Project documentation Connecting repositories to deployment platforms Showcasing projects to recruiters ⭐ My Current Preference For my projects, my preferred setup is: 🎨 Frontend → Vercel ⚙️ Backend → Railway 💻 Source Code → GitHub The best platform ultimately depends on the project's requirements, such as model size, RAM, CPU, database requirements, traffic, and budget. I'm currently learning more about deploying Machine Learning applications and comparing different cloud platforms. 🚀