Author: Fritz

A few weeks ago I looked at the speed of Core ML models running on various Apple devices. Apple’s new A12 Bionic chip paired with Core ML 2 made the latest generation of iPhones and iPads more than 10x faster than previous generations.

While faster is usually better, large differences in performance across devices make it hard for developers to keep user experience consistent. Apple’s (relatively) small product family and aggressive upgrade strategy mitigates this problem to some degree.

Continue reading “Benchmarking TensorFlow Mobile on Android devices in production”

Machine learning and AI are having more and more influence on mobile tech nowadays. Hardware is becoming more and more AI-capable, and machine learning and AI methods are being integrated in mobile apps to optimize and enhance user experiences.

In this tutorial, we’re going to apply machine learning methods provided by the TensorFlow Lite library for the purpose of image classification in a Flutter app. Flutter—as introduced and acknowledged by Google—has a tendency to produce and support libraries related to machine learning.

Continue reading “Dog Breed Classification on Mobile with Flutter and TensorFlow Lite”

Generally speaking, human beings can recognize objects without too much effort or consideration. However, recognizing objects in images can be a very difficult task in the field of computer vision.

There are many use cases in which this difficulty becomes evident. One specific use case involves license plate detection—it’s particularly a big challenge because of the differences inherent in the plates (i.e. the objects) themselves: different sizes and styles, the conditions and lighting under which the images of the plates are captured, etc.

Continue reading “License Plate Recognition, Detection, and Plate Number Extraction on iOS”

As a first small step towards a federated learning platform that supports mobile and wearable devices (in particular, devices within the Apple ecosystem) I’ve being developing a Swift library called SwiftCoreMLTools that mimics in Swift a subset of the functionalities of Apple’s CoreMLTools Python library.

Continue reading “Core ML On-Device Training, with Transfer Learning from Swift for TensorFlow Models”

Apple is truly investing in testing tools and technologies, which is a great thing for app developers. In fact, Apple announced parallel testing support for the XCTest framework at WWDC 2018 in its platform state of the union session. There was a separate session on What’s New in Testing, describing new features in Code Coverage, XCTest, and XCUITests, which included parallel testing.

Continue reading “The Fast, Furious, and Flaky: Continuous Integration with Xcode 10 Parallel Tests”

If you at all follow the economics of mobile platforms, then you’ve almost inevitably heard about (and likely saw) Epic’s recent viral campaign in response to Apple’s decision to ban the incredibly popular, free-to-play game Fortnite from the App Store.

You can read much more about what’s happening between Epic and Apple, but it boils down to this: The App Store has banned Fortnite for violations of its terms of service, specifically because Epic has been using its own in-game payment system. This makes some sense, given Fortnite’s status as an elite freemium gaming experience.

Continue reading “Monetizing Mobile Machine Learning”

In a previous article, I described how to utilize speech recognition in Android. This consisted of capturing user speech, processing it, and implementing it.

But what if that process is reversed, and you want to take a text input and output speech? This type of system is, so-called, text-to-speech (TTS). TTS software, in general, creates a computer-generated voice that’s also considered as an assistive technology tool.

Continue reading Make your Android apps talk with Text-To-Speech

While flashy deep learning research grabs headlines, what happens to models after they are trained is equally important. To build a great product, you need to plan for the entire lifecycle of machine learning models, from data collection and training to deployment and monitoring.

This becomes even more critical when deploying ML models outside of the cloud, directly in mobile apps where you face the unique challenges of supporting multiple platforms, hundreds of chipsets, and billions of installs.

Continue reading “The Lifecycle of Mobile Machine Learning Models”

Machine learning, deep learning, neural networks, artificial intelligence. You can’t work a day in tech without coming across one of these buzzwords.

For a developer just looking to get started, it’s hard to wade through the jargon and ever-changing tools. This post is the first in a broader series we’re working on at Heartbeat on to help people get up to speed or stay current.

Continue reading “Mobile Machine Learning 101: Glossary”