<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>ML on The Final Artefact</title><link>https://www.thefinalartefact.xyz/tags/ml/</link><description>Recent content in ML on The Final Artefact</description><generator>Hugo</generator><language>en-gb</language><lastBuildDate>Thu, 24 Jul 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://www.thefinalartefact.xyz/tags/ml/index.xml" rel="self" type="application/rss+xml"/><item><title>Bring your Python ML Model to iOS App in under Three Minutes</title><link>https://www.thefinalartefact.xyz/post/python-models-app/</link><pubDate>Thu, 24 Jul 2025 00:00:00 +0000</pubDate><guid>https://www.thefinalartefact.xyz/post/python-models-app/</guid><description>&lt;p&gt;&lt;a href="https://www.thefinalartefact.xyz/post/python-models-app/images/phonedemo.gif" target="_blank" rel="noopener noreferrer"&gt;
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&lt;p&gt;Integrating Python-based machine learning models into iOS applications can be challenging, particularly when converting models into a Swift-compatible format. This example will demonstrate a simple image classification task using the Fashion-MNIST dataset and CoreML conversion tools. The goal is to illustrate the effort required to deploy small-to-medium complexity ML models within iOS applications. The demonstration is based on a Convolutional Neural Network (CNN) built with PyTorch, but the concepts apply broadly to other Python-based models as well.&lt;/p&gt;</description></item></channel></rss>