You built this amazing machine learning model—this one, let’s say—but now what?
How do you take your model and turn it into something that you can display on the web? How do you turn it into something that other people can interact with? How do you make it useful?
You deploy it!
Continue reading “The brilliant beginner’s guide to model deployment”
People are passionate about pizza. Deep dish, thin crust, traditional pan, flatbread — and those are just styles of crust, to say nothing of topping preferences. Pineapple? Anchovies??
With such a wide variety of styles and preferences to swear by, it’s important for pizza lovers to have an authoritative source for honest and legitimate reviews — and the chance to share their love of pizza with others.
Continue reading “One Bite pizza reviews: Building community trust with mobile machine learning”
WebViews in Android are often seen as a lazy programmer’s tool—Don’t have enough time to build a particular screen for your app? Just use a WebView to display a page from your website instead. Users won’t be able to tell the difference…right?
Jokes aside, while you’ll often hear other developers discouraging the use of WebViews, they’re often necessary. For example, if you want to make a transaction with your bank or credit card, that transaction screen is a WebView.
Custom login screen for apps where security is a concern is often a WebView instead of a native widget. Google Mobile Ads (or Admob) also uses a WebView to display Banner and Interstitial ads on your app.
If you’re looking for help in easily adding machine learning to your Android apps, you’ve come to the right place. In this article, we’ll learn how to get started, implementing a task known as text recognition.
Continue reading “Recognize Text in Images on Android with Firebase’s ML Kit”
I’ve always thought of community as something akin to a party. A thing that forms largely by happenstance, where the people involved are connected through discrete and clear purposes: Socializing, relaxing after a long work week, meeting new people.
So when I found myself in the role of building the Heartbeat community, I initially struggled to see a bigger picture. I had the vague sense that the main priority should be centered on putting the needs and desires of community members first—much in the same way I’d want to be a good host at a party.
There exist a plethora of articles on quantization, but they generally cover only the surface level theory or provide a simple overview. In this article, I’ll explain how quantization is actually implemented by deep learning frameworks.
Before getting into quantization, it’s good to understand the basic difference between two key concepts: float (floating-point) and int (fixed-point).
I’m by no means a developer, engineer, or coder of any kind. Though I do read a lot of tutorials that include code implementations, and occasionally peruse repos to explore what folks are building, the most I’ve ever done is add some CSS style elements to a button within an HTML file.
So on the surface, the idea of me being able to build a cross-platform, mobile-ready machine learning model is kind of absurd.
Developers using Fritz AI can now track the body positions of people in images and video with Pose Estimation.
Deploying machine learning-based Android apps is gaining prominence and momentum with frameworks like TensorFlow Lite, and there are quite a few articles that describe how to develop mobile apps for computer vision tasks like text classification and image classification.
But there’s very much less that exists about working with audio-based ML tasks in mobile apps, and this blog is meant to address that gap — specifically, I’ll describe the steps and code required to perform audio classification in Android apps.
Continue reading “Audio Classification in an Android App with TensorFlow Lite”
For farm owners in developing countries, having access to the right education and tools to aid in crop production is important to improving their livelihoods. PlantVillage, a research group out of Pennsylvania State University, leverages machine learning in areas of limited connectivity to diagnose plant disease and advise farmers on how to treat their crops.
