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.
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.
I recently came across an excellent article from Signal where they introduced a new feature that gives users the ability to automatically blur faces—incredibly useful in a time when protestors and demonstrators need to communicate while protecting their identities.
In the article, Signal also hinted at technologies they’re using, which are strictly platform-level libraries. For iOS, I would guess they have used Vision , an API made by Apple to perform a variety of image and video processing.
Continue reading “Automatically Pixelate Faces on iOS using Face Detection with Native Swift Code”
For developers, the ability to run pre-trained models on mobile signifies an important shift towards edge computing. By being able to perform data processing straight from the user’s phone, private data remains in their hands, apps run more smoothly without having to wait for cellular networks, and your company’s cloud bill is significantly reduced.
Fast, responsive apps can now run complex machine learning models. This technological shift will usher in a new wave of app development by empowering product owners and engineers to think outside the box.
In this tutorial, we’ll build a Recurrent Neural Network (RNN) in PyTorch that will classify people’s names by their languages. We assume that the reader has a basic understanding of PyTorch and machine learning in Python.
Continue reading “Detecting the Language of a Person’s Name using a PyTorch RNN”
Lens Studio can gives one the ability to create some truly amazing augmented reality effects for Snapchat. I’ve found the performance and speed with which these lenses display to be very smooth, and this is due in part to the strict requirements that Snapchat has for the quality of submitted lenses.
However, this performance comes with a price. One of the most challenging things any lens creator faces is fitting everything into the 4mb of space that is allowed.
Continue reading “Tips and Tricks to Keep your Lens Within Lens Studio’s 4mb Limit”
This is the second part in a two-part series. I suggest you read the first part first. Interact with the app via the App link
The goal of the second part of this tutorial series is to deploy the machine learning wine quality prediction web application built in part 1—this time in the cloud.
Continue reading “Deploy a Machine Learning Model as a Web Application (Part 2)”
