Free Tools to Deploy ML Models as a Student

If you’re a student or a fresher trying to break into the AI industry, learning to deploy your models is no longer a nice-to-have skill. At least one project mentioned on your resume should involve end-to-end deployment. It shows you can complete the entire lifecycle of a Machine Learning project. In this article, I’ll introduce you to some of the best and free tools available for deploying ML models as a student.

But, Why Bother Deploying ML Models?

Think like an Engineer. A model in a notebook is an academic achievement. Although not all ML models need to be deployed, a deployed model is still a product. The latter is what companies pay for. By deploying your work, you demonstrate:

1. You can turn your code into something tangible and usable.
2. You understand the whole process, not just the training part.
3. A link to a live app is infinitely more impressive than a link to a GitHub repository with a static notebook.

Now, let’s break down the best and free tools to get you started for deploying ML models as a student.

Free Tools to Deploy ML Models as a Student

Streamlit: The Fastest Way from Data to App

Think of Streamlit as the Pythonic way to build a web app. If you can write a Python script, you can build a beautiful, interactive data application in minutes.

It’s built for data scientists, not web developers. You don’t need to know any HTML, CSS, or JavaScript. Its primary goal is speed and simplicity, making it perfect for creating dashboards, data explorers, and simple model front-ends.

You can use Streamlit to build interactive dashboards, data visualization tools, and straightforward portfolio projects where the focus is on quickly showcasing data and model interaction.

Here are some resources to master Streamlit for deployment:

1. End-to-End Chatbot using Streamlit
2. Creating Interactive Dashboards using Streamlit

Gradio: The Specialist for ML Model Demos

If Streamlit is a general-purpose tool for data apps, Gradio is a specialized tool laser-focused on creating a simple demo interface for your machine learning model.

It’s swift for demoing. It automatically creates the right input/output components for your model. If your model takes an image and returns text, Gradio gives you an image upload box and a textbox, all with one line of code.

You can use Gradio for creating quick, shareable demos for your models, especially for computer vision, NLP, and audio tasks. It’s the go-to for hackathons and quick proofs-of-concept.

Here are some resources to master Gradio to build quick and shareable demos of ML models:

1. Build a Live and Shareable ML App in 5 Minutes
2. Gradio Documentation

Flask: The Path to a Professional API

Streamlit and Gradio are fantastic for creating UIs. But in the real world, models are often served via an API. This is where Flask comes in. It doesn’t create a UI; it creates an endpoint that other applications can call to get predictions.

This is how most production models are deployed. Learning Flask teaches you the fundamentals of HTTP requests, routing, and how software components talk to each other over the internet. This skill is a massive plus for any ML Engineer role.

You can use Flask to understand the core principles of model serving, build custom web applications, and prepare for ML Engineering roles that require integrating models into larger software stacks.

Here are some resources to master Flask to learn serving ML models as APIs:

1. Packaging ML Models as an API for Deployment
2. Developing AI Applications with Python and Flask

Hugging Face Spaces: Your Free Deployment Server

You’ve built your app with Streamlit or Gradio. Now, where do you host it so the world can see it? You can use Hugging Face Spaces.

It’s the GitHub for ML demos. It offers one-click deployment for Streamlit and Gradio apps, completely free. All you need to do is upload your code and a requirements.txt file.

Hugging Face Spaces is for everyone starting. It’s the easiest, fastest, and most cost-effective way to get your Streamlit and Gradio apps online and into your portfolio.

Here are some resources to master Hugging Face Spaces as a deployment server:

1. Understanding Hugging Face Spaces
2. Creating Demos with Spaces and Gradio

Final Words

So, here’s what you need to do next:

1. Pick a model you’ve already trained.
2. Start with Streamlit or Gradio. Wrap your model’s prediction function in a simple UI.
3. Deploy it for free on Hugging Face Spaces.
4. Share the link. Add it to your resume, your LinkedIn profile, and your next job application.

This single step will put you ahead of 80% of your peers. I hope you liked this article on free tools available for deploying ML models as a student. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.