Mobile machine learning is still, relatively speaking, in its infancy. As such, new developer tools, frameworks, and environments are regularly in flux.
It can be hard to keep up with the rapid advancements happening at this emerging intersection.
Mobile Machine Learning: Development tools and frameworks
To help you navigate the mobile ML landscape, we’ve curated a number of helpful articles that explore these tools—posts that compare them, provide their benchmarks, outline their benefits and limitations, and discuss what might be next in the mobile ML world.
Note: The following resources are primarily non-code. For guides and example projects to start building with ML on mobile, check out some of our other Heartbeat Collections: iOS Computer Vision, iOS NLP, and Android Computer Vision.
Using People Detection to Enforce COVID-19 Safety Measures (Part I)
Understanding and Deploying Object Detection Models
— by Harsh Sharma
Exploring Use Cases of Core ML Tools
Core ML Tools is a Python library that helps you convert ML models to Core ML format. But it can also do a lot of other things.
— by Anupam Chugh
PyTorch Mobile: Exploring Facebook’s new mobile machine learning solution
PyTorch enters the mobile machine learning game with its experimental mobile deployment pipeline. We explore what’s included in the release, what isn’t and discuss the implications for mobile ML.
— by Austin Kodra
Comparing Firebase ML Kit’s Text Recognition on Android & iOS
Comparing Google’s Firebase ML Kit Text recognition feature on Android and iOS.
— by Zain Sajjad
Machine Learning models on the edge: mobile and IoT
Edge devices are becoming increasingly important — here’s how machine learning works with them.
— by Justin Gage
What’s new in Core ML 3
A detailed exploration of Apple’s updates to their mobile machine learning framework, and the implications for developers and ML engineers.
— by Jameson Toole
Advanced Tips for Core ML
In this post, you’ll learn 4 advanced tips to help manage your mobile machine learning projects as they grow in scale and complexity.
— by Jameson Toole
Train a MobileNetV2 + SSDLite Core ML model for object detection — without a line of code
Learn how to use the MakeML webapp to train a custom object detection Core ML model ready for use on iOS.
— by Alexey Korotkov
MakeML’s Automated Video Annotation Tool for Object Detection on iOS
Track and label video frames automatically to create a custom dataset for object detection models on iOS.
— by Alexey Korotkov
Comparing Mobile Machine Learning Frameworks
In-depth analysis of Firebase ML Kit, Clarifai, Fritz AI, Skafos.ai, & Numericcal.
— by Zain Sajjad
Build iOS-ready machine learning models using Create ML
Learn how to use Apple’s Create ML to build a custom image classification model that’s ready to implement in an iOS app.
— by Navdeep Singh
Neural Networks on Mobile Devices with TensorFlow Lite: A Tutorial
A practical, end-to-end guide on how to build a mobile application using TensorFlow Lite that classifies images.
— by SAGAR SHARMA
iOS 12 Core ML Benchmarks
Performance benchmarks for Core ML in iOS 12, on Apple’s A12 Bionic Processor.
— by Jameson Toole
Machine learning on mobile devices: 3 steps for deploying ML in your apps
How does machine learning work inside mobile apps? Should you include ML features in your app? This article will help you answer these questions and more.
— by Déborah Mesquita
Benchmarking TensorFlow Mobile on Android devices in production
Measuring TensorFlow Mobile runtime speeds across Android devices.
— by Jameson Toole
Machine learning on iOS and Android
Exploring machine learning on mobile with benefits, use cases, and developer environments.
— by Austin Kodra
Comparing MobileNet Models in TensorFlow
Compared to similar models, MobileNet works better with latency, size, and accuracy. This is especially beneficial for models deployed to mobile for real-time inference.
— by Harshit Dwivedi
Core ML vs ML Kit: Which Mobile Machine Learning Framework Is Right for You?
A comparison of Core ML, Apple’s mobile machine learning platform, and ML Kit, Google’s solution on the Firebase platform.
— by Sahil Chaudhary
Reverse Engineering Core ML
When a developer deploys a machine learning model to mobile, they lose control over how the model is accessed or used. This post shows how to take a Core ML model and reconstruct the original model.
— by Christopher Kelly
Train and Ship a Core ML Object Detection Model for iOS in 4 Hours — Without a Line of Code
Use MakeML to train your own mobile-ready machine learning model without writing a line of code.
— by Alexey Korotkov
Embracing Machine Learning as a Mobile Developer
Machine learning can be intimidating for mobile developers. But it doesn’t have to be. Learn why on-device ML can be valuable and explore what tools and platforms are already available.
— by Harshit Dwivedi
How TensorFlow Lite Optimizes Neural Networks for Mobile Machine Learning
Learn how TensorFlow Lite empowers mobile developers to convert and deploy machine learning models to mobile devices.
— by Airen Surzyn
Machine Learning on iOS 12 and the New iPhone X Series
Exploring Apple’s advancements in on-device machine learning for iOS with the release of iOS 12 and the iPhone X series.
Comparing Apple’s and Google’s on-device OCR technologies
TL;DR…Apple’s Text Recognition is Crushing it
— by Omar M’Haimdat
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