BolajiAyodeji/deploy-ml-web-workshop
In this workshop, you will learn how to build a machine learning model using Python/Scikit-Learn, turn the model into an API using Python/Flask, test the API, build web applications using HTML/CSS/JavaScript/Nextjs, and deploy it to the web for global usage by end-users.
This workshop helps machine learning engineers turn their trained models into interactive web applications that end-users can access globally. It guides you through creating an API for your model, building a web interface, and deploying everything to the cloud. The output is a live, user-facing web application powered by your machine learning model.
Use this if you are an ML engineer struggling to move your models from development notebooks to a live, usable web service for others.
Not ideal if you are looking for advanced model optimization techniques or highly scalable enterprise deployment strategies, as this focuses on foundational deployment steps.
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23
Forks
5
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Feb 25, 2026
Commits (30d)
0
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