Machine Learning Projects to Learn Deployment

The biggest hurdle for most aspiring data scientists isn’t building a model; it’s deploying it so that others can actually use it. As your mentor in this field, I’m here to tell you that building a great model is only half the battle; the real value, and what companies pay for, is making that model usable. So, make sure to learn deployment. In this article, I’ll take you through three Machine Learning projects you should try to learn deployment practically.

Machine Learning Projects to Learn Deployment

Here are three Machine Learning projects that will teach you deployment step-by-step. Try these projects to learn how to build something that works, that can be shared, and that proves you know how to deliver value.

End-to-End Chatbot with Streamlit

This is your first rite of passage. It forces you to think end-to-end. It’s not just about the Natural Language Processing (NLP) model you’ve built; it’s about the entire user experience. Streamlit is a pure Python library that enables you to develop and share web apps without needing to touch HTML, CSS, or JavaScript.

Here’s what you will learn from this project:

Front-end Basics: You’ll create a user interface (UI) to interact with your model.
Model Integration: You’ll connect your pre-trained model to the UI, handling user input, processing it, and displaying the model’s response.

Find a guided example to build an end-to-end chatbot using streamlit here.

Live and Shareable ML App with Gradio

If Streamlit is for building and sharing beautiful apps, Gradio is for rapid prototyping and live demos. It’s the go-to tool for showing off a computer vision model or a generative AI masterpiece to a non-technical audience. Think of it as the ultimate show-and-tell for machine learning engineers. With just a few lines of code, you can create a link and instantly share your model’s capabilities.

Here’s what you will learn from this project:

Rapid Prototyping: You’ll learn to create a minimal, functional UI for your model in minutes, not hours.
Model Demoing: You’ll master the art of creating a compelling demo. This is a critical skill for interviews.

Find a guided example to build a live and shareable app using Gradio here.

Packaging ML Models as an API for Deployment

This project is about building the brain behind the operation. An API (Application Programming Interface) is a back-end service that allows other applications to use your model. For example, a web app might send a user’s text to your API, and the API sends back a sentiment score. This is how machine learning models are integrated into larger software systems in the real world.

Here’s what you will learn from this project:

Back-end Development: You’ll use frameworks like Flask or FastAPI to create a web server.
Testing Your API: You will learn to test your API as it’s tested in the real world using tools like Postman.

Find a guided example to package ML models as an API for deployment here.

Summary

So, here are three Machine Learning projects that will teach you deployment step-by-step:

Pick one project that excites you and commit to building it this week. I hope you liked this article on Machine Learning projects that you should try to learn deployment. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.