🚀 My Experience Choosing Deployment Platforms for ML Projects

kavya g — Mon, 10 Aug 2026 13:56:37 +0000

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. 🚀

MachineLearning #DataScience #Deployment #Vercel #Railway #GitHub #HuggingFace #Render #Python #Backend #Frontend

DEV Community: kavya g

🚀 My Experience Choosing Deployment Platforms for ML Projects

MachineLearning #DataScience #Deployment #Vercel #Railway #GitHub #HuggingFace #Render #Python #Backend #Frontend