Whenever someone asks me to explain what mobile machine learning is, I instinctively bring up Snapchat as a core example.
In 2015, the incredibly popular social content platform added Lenses to their mobile app—if you’ve ever played with Snapchat, you know these well. They’re essentially augmented reality (AR) filters that give you big strange teeth, turn your face into an alpaca, or trigger digital brand-based experiences.
SwiftUI is defined as being declarative and reactive. The former is what allows us to write out our UI, which we can do in a very clean and organized fashion. The latter is what brings our UI and data closer together than ever.
We’ve been working on an app that allows us to view our team and dive into the profile pages of each member.
Continue reading “SwiftUI: Observables, View Hierarchy, and Putting Them All Together”
In order for data science teams to outsource annotation to a managed workforce provider — also known as a Business Process Outsourcer (BPO) — they must first have the tools and infrastructure to store and manage their training data.
Data management tools and infrastructure should support R&D product management teams, outsourced labeling teams, and internal labeling and review teams, working together in a single centralized place with fully transparent oversight.
Continue reading “How to Scale Training Data – Complete Guide”
ChAR Digital was founded Benjamin Paruzynski, an official Snapchat Lens Creator and Creative Partner. Working with brands to shape their digital marketing, Paruzynski and his team create lenses, filters, and effects.
Though he’s kept pretty busy between projects, I managed to catch up with the CEO to learn more about his digital journey.
Every day, our world is becoming more mobile, with more than 2 billion smartphones circulating globally. As such, mobile development has the potential to reach all corners and aspects of the modern world. This is equally true when it comes to machine learning.
Building machine learning models that we can use on mobile will open endless avenues for creativity, automation, and efficiency. But there’s a significant knowledge gap between mobile development and machine learning.
In this post, we are going to understand the underlying concepts of semantic image inpainting and various techniques developed for the same.
However, we are going to focus on an effective and elegant state-of-the-art context encoder and generative model based approach for image inpainting.
Continue reading “Semantic Image Inpainting with Context Encoders”
Regular attendees of tech conferences might have noticed that pushing toward AI and machine learning is one thing that’s consistent across all tech giants, including Apple , Google , Amazon, and Microsoft.
For example, this year’s Google I / O was largely focused on machine learning, artificial intelligence, and how it’s going to revolutionize the world.
And me, as an Android developer and someone who has a bad case of FOMO, I started looking into any and all resources that could help me get a kickstart with using machine learning and AI in my development projects.
Continue reading “Embracing Machine Learning as a Mobile Developer”
Tween Manager is used in making custom animations within Lens Studio. This helps you make your lenses more engaging and captivating. You also don’t need to write any code. In this article, you will learn how the different types of tweens works.
Selfies are an art form. If you’re going to put your face out there, it’s got to look good. I’ve seen plenty of apps claim to offer the best filters, stickers, and retouching options, but they most of them just fall flat. The Portrait app by Img.ly is one of the few that offers something truly unique.
They use machine learning to automatically extract our face from the background in order to create awesome, poster-like styles that look different than anything else out there.
In our post from about a month ago, we compared two of the major on-device text recognition SDKs on iOS: Firebase’s ML Kit & Tesseract OCR. The results were pretty one-sided, as ML Kit outclassed Tesseract on many of the predictions and showed much better accuracy even when it failed.
Continue reading “Comparing Firebase ML Kit’s Text Recognition on Android & iOS”
