iOS Machine Learning Deployment with Core ML and Vapor
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Published 5/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 45m | Size: 1.6 GB
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 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.How This Course Will Benefit YouEnd-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
Homepage:
Kod:
https://www.udemy.com/course/ios-machine-learning-deployment-with-core-ml-and-vapor/
Download link
rapidgator.net:
Kod:
https://rapidgator.net/file/5d45ce779f22462fed210ee3cde52caf/kvvgn.iOS.Machine.Learning.Deployment.with.Core.ML.and.Vapor.part1.rar.html
https://rapidgator.net/file/9d29abafa4f1a66d9139ad6a9afe8021/kvvgn.iOS.Machine.Learning.Deployment.with.Core.ML.and.Vapor.part2.rar.html
nitroflare.com:
Kod:
https://nitroflare.com/view/F7BBB8ED4FFEDD5/kvvgn.iOS.Machine.Learning.Deployment.with.Core.ML.and.Vapor.part1.rar
https://nitroflare.com/view/02B6E7D6220421F/kvvgn.iOS.Machine.Learning.Deployment.with.Core.ML.and.Vapor.part2.rar
