iOS Machine Learning Deployment with Core ML and Vapor

Build and Deploy Intelligent iOS Apps from Python to Production

What you'll learn
- Clean and prepare real-world datasets using Python and Pandas
- Train a machine learning model with scikit-learn
- Convert your model to Core ML format for iOS integration
- Build a SwiftUI app that makes real-time predictions
- Deploy the model to a Vapor server and create a REST API for ML inference

Requirements
- Basic understanding of Python and machine learning concepts
- Familiarity with Swift and SwiftUI development
- Xcode installed on your Mac (for Core ML and SwiftUI)
- Basic experience using the terminal and running command-line tools
- Comfortable working with JSON and REST APIs

Description
iOS Machine Learning Deployment with Core ML and Vaporis 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 intoPython, where you'll work with real-world data sourced fromKaggle. You’ll learn how to clean and preprocess this data, fix incorrectly formatted columns, handle missing values, and apply essential data transformation techniques such asstandardizationandlabel 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 usingscikit-learn, one of Python’s most widely used ML libraries. You'll then convert the model into Apple’sCore MLformat usingCore ML Tools, preparing it for smooth integration into iOS apps.

But we don’t stop there. The second half of the course focuses onreal-world deployment. You’ll embed your Core ML model into aSwiftUI-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 introduceVapor, 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.

How This Course Will Benefit You

End-to-End Knowledge: Gain the complete pipeline experience — from data preprocessing and model training to mobile integration and backend deployment.

Cross-Disciplinary Skills: Learn how to combine Python-based data science with Swift-based mobile and server development — a powerful, rare skill set in the job market.

Portfolio-Ready Project: Walk away with a fully functional iOS app backed by a deployed machine learning model — perfect to showcase in job interviews or on your GitHub.

Production-Grade Deployment: Understand how to build scalable, real-time ML applications that can serve predictions via API endpoints.

Boost Your Career: Whether you're a Python developer exploring mobile development, or an iOS developer stepping into ML, this course will add significant value to your toolkit and resume.

Future-Proof Skills: With AI becoming central to modern apps, knowing how to build and deploy ML-powered features is becoming a must-have skill.

Whether you’re a data scientist looking to bring your models to iOS or a Swift developer aiming to expand into machine learning, this course will give you the tools and confidence to build and deploy smarter, production-ready applications.

Who this course is for:
- iOS developers who want to integrate machine learning into their apps
- Python developers looking to deploy ML models in iOS environments
- Machine learning enthusiasts who want to take their models from training to real-world usage
- Full-stack developers interested in combining frontend (SwiftUI) and backend (Vapor) with ML
- Anyone eager to build intelligent, production-ready iOS applications using modern tools
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