Be it, kids or adults, everyone has Snapchat installed on their smartphones. Be it clicking funny pictures, creating videos with filters, creating bitmojis, maintaining snap streaks with friends, or anything entertaining, Snapchat has become everyone’s favorite social media platform. Well, this addiction is a topic of concern, whether it is still debatable or not, because behind the back of this app, what is going on, nobody knows. But yeah, today we are actually going to learn how Snapchat is using machine learning.
We are going to know each and every possible aspect of machine learning to know how Snapchat works with this.
Creating Lenses and Filters
What are Snapchat Lenses and Filters?
Snapchat has two attractive features. The first is lenses, and the second is filters. Lenses are amazing, playful Augmented Reality experiences that totally change the world around you and make it more fun for you. Whereas filters are defined as the amazing, jolly, creative effects that add up when you click a snap. Some examples of lenses and filters are Disneyland Geo-filter, The Peanuts Movie, Malibu Rum, etc.
How to use a lens or filter?
Hold your camera steady and reframe your snap with the filter or lens you wish to use. The lens/filter design takes over the snap, and voila! a beautiful snap is in front of you.
It’s very easy to find a lens or filter. Open Snapchat. As soon as you open it, the snap camera opens. Just swipe to the right and you will get all the beautiful filters and lenses.
How does AIML play a role in lenses or filters?
Snapchat uses AI-powered lenses with small machine learning models for filters and lenses. It is used to detect a face, differentiate the structure and features within it, and then create a 3D model of the face.
This process is completed using ‘Image Processing’; ‘Facial Landmarks’—plotting points on the face and comparing them with collected data sets on the face; and active ‘Shape Modelling’, which creates a virtual face and aligns it with the actual face.
Targeting Ads
What are the ads?
Advertisements are marketing communications to put a company in front of the world. Snapchat uses a lot of ads to grow its reach among a billion people.
What is the role of ads in Snapchat?
Snapchat calls out the ads as goal-based bidding. As we know, Snapchat is a very big platform, with over 53.5 million downloads. This gives an opportunity to all the advertisers to showcase themselves globally and come up with meaningful results.
How does AIML help Snapchat in ads?
Well, Snapchat is the only self-serve advertising platform. It uses iterative improvements to advertising optimization on the platform. This targeting technique allows the advertisers to guide their message whether they value ad engagement more frequently or not. Just like when we install the app when we are having a glance at people’s feeds, etc. at that time.
SnapML
What is SnapML?
SnapML (Snap Machine Learning) is a library that is used to train generalized linear models. It was developed by IBM to save training time as a bottleneck for machine learning. It has a total of three SnapML packages:-
- SnapML local
- SnapML mpi
- SnapML spark
How does SnapML work?
SnapML allows you to make more beautiful and amazing lenses, and this thing only happens with machine learning.
You just need to be in a machine learning model that allows you to apply any art style to the camera. You can use custom segmentation masks. You can also attach images to custom detected objects and understand what exactly is going on inside the screen.
Conclusion
Snapchat is a very big platform, and it has many things hidden inside it. The sparkling filters that you are seeing today are all because of the beautiful work of machine learning behind it. The platform has used many techniques to upgrade itself, like targeting ads and having a good marketing strategy, and all of this is possible because of machine learning. Finally, we got to know about this great SnapML library, which was actually created with the help of IBM. So yeah these are some of the features of how Snapchat is using machine learning.
Read More:- What is an algorithm, Types of algorithms, and Uses
