iOS Machine Learning Deployment with Core ML and Vapor

On Udemy

About this course

iOS Machine Learning Deployment with Core ML and Vapor is a comprehensive, hands-on course designed to bridge the gap between Python-based machine learning and Swift-based deployment. This course is ideal for developers who want to move beyond just training models and learn how to integrate them into real-world iOS applications — all while using modern tools and best practices.

We begin by diving into Python, where you'll work with real-world data sourced from Kaggle. You’ll learn how to clean and preprocess this data, fix incorrectly formatted columns, handle missing values, and apply essential data transformation techniques such as standardization and label encoding. These foundational skills ensure your model is robust, reliable, and production-ready.

Once your data is properly prepared, you'll train a machine learning model using scikit-learn, one of Python’s most widely used ML libraries. You'll then convert the model into Apple’s Core ML format using Core ML Tools, preparing it for smooth integration into iOS apps.

But we don’t stop there. The second half of the course focuses on real-world deployment. You’ll embed your Core ML model into a SwiftUI-based iOS application, learning how to design an intuitive user interface and make real-time predictions using your trained model. You’ll also learn how to send and receive data from the model in a user-friendly way.

To complete the full-stack experience, we introduce Vapor, Apple’s open-source server-side Swift framework. You'll learn how to host your Core ML model on a Vapor server and build a RESTful API that iOS apps can communicate with. This demonstrates how to turn your machine learning models into live, accessible services — an essential skill in today's data-driven app development landscape.