As we make progress in the era of edge computing, the demand for machine learning on mobile and edge devices seems to be increasing quite rapidly.
ML-enabled services such as recommendation engines, image and speech recognition, and natural language processing on the edge (to name a few) are growing, as is the need for processing large amounts of data with reduced latency.
Continue reading “MACE: Deep learning optimized for mobile and edge devices”
A few months ago, Snapchat released a new feature where AI developers and creatives can easily integrate their own custom lenses using machine learning models — one of these features is the ability to track labeled objects on the view and project fun and immersive AR effects. We’ll cover this ML task in this article.
Continue reading “Build a Snapchat Lens to Welcome New Texans Using Fritz AI”
I’m a software engineer by trade. But I seem to keep dipping my toes into the machine learning and data science communities. There are some awesome libraries out there, but I admit I’ve had a difficult time wrapping my head around many of them.
Surprisingly, it’s not the math. It’s actually the code itself that I find myself getting stuck on. It’s understandable. Tensor algebra is hard enough without needing to worry about style guides and code reviews.
Continue reading “How great data scientists can write better code”
Machine learning unlocks a wide range of possibilities when it comes to AR development—nobody has proved that more than Snapchat, whose immersive augmented reality Lenses work so well in large part because of the ML models running underneath them (things like face tracking, background segmentation, and more).
With SnapML, though, Snapchat has opened up this expansive capability to its community of Lens Creators, allowing them to implement custom machine learning models in their Lenses.
Continue reading “Demystifying common use cases for SnapML in Lens Studio”
Lens Studio’s functionalities can be extended by writing your own custom code in JavaScript. For instance, you can write your own code to target various events in your Lens and trigger specific actions.
Lens Studio is already powerful without custom code, but the ability to bring your own code to the platform is just amazing. Without further ado, let’s jump straight in and see how this can be done.
Privacy has always been a hot-button topic of discussion when it comes to the age-old debate of iOS vs Android.
While I’m not going to pick a side here, Android has made some radical changes in recent years concerning privacy, and the overhaul of the permission handling system is probably the biggest one of those changes.
Below is the transcript for the above video explaining the step-by-step process of downloading Fritz AI pre-trained models and opening them in Lens Studio.
“Hey everyone, my name is Emilie and I’m the content manager for Heartbeat here at Fritz AI. Today I’m going to show you just how easy it is to create a Lens Studio profile and open one of our many pre-trained SnapML models.
Continue reading “How to Load Fritz AI’s Pre-Trained Models into Lens Studio”
Follow along as we build a custom, ML-powered face mask color changing Snapchat Lens.
You’ll learn how to use Fritz AI Studio to:
Continue reading “Custom Face Mask Segmentation for Lens Studio with Fritz AI (Video + Transcript)”
We’re committed to making it easier to work with machine learning across mobile platforms. That’s why we’re happy to share that we’ve created a number of pre-trained SnapML project templates that are ready-to-use in Lens Studio upon download.
This means that Snapchat Lens Creators working with Fritz AI now have an even easier way to get started with machine learning in their projects. Just download the project zip file, open the Lens Studio Project, and let your imagination run free. That’s it!
Continue reading “Introducing Fritz AI Pre-trained Projects for SnapML in Lens Studio”
While machine learning might seem fun and exciting from the outside, building a dataset and training the model is as dull as it gets.
However, don’t take this the wrong way—model training is a very critical part of the entire process and shouldn’t be overlooked.
Continue reading “Training a TensorFlow Lite Image Classification model using AutoML Vision Edge”
