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Articles by Canva Engineering
A Journey through Color Space with FFmpeg
Video For those who want to understand color spaces, transform videos from one color space to another, or read how I almost went crazy working out why my Canva-generated videos looked slightly off, color-wise by Sven Schindler This is the sentence that started it all. A user wanted to export a number of colorful Canva slides to a video file. I just recently joined Canva’s video group and thought this might be a great starter task.
Understanding a Diverse User Base with Frequency Segmentation at Scale
Data-driven segmentation At Canva, our mission is to empower everyone to design anything. Over the years, the definition of "anything" has expanded: from social media material, resumes, and cards, to infographics, presentations, print, and video. Canva's user base is also diverse, with widely varying needs and usage patterns. We have marketers who make multiple social media posts per week, and casual users who only return every few months when they need to design a birthday card or invitation.
Deep Learning for Infinite (Multi-Lingual) Keywords
Machine Learning How we used a CLIP-inspired model to suggest keywords for template labeling in multiple languages by Sachinthaka Abeywardana At Canva we have millions of pieces of content, across many languages to serve our over 100 million monthly active users around the world. In order for them to be surfaced during search, keywords are a necessity. It is common practice in most websites to include keywords in order to be indexed on search engines.
Search Pipeline: Part I
Search and Relevance How we are rebuilding Canva’s search stack and pipeline by Stuart Cam This two-part blog post details the challenges we faced within the Search and Recommendations group in building a scalable search architecture. In part I, we’ll discuss the challenges, and in part II, we’ll break down the details of our search architecture, and how we take a platform-first approach to enable Canva to build a world-class search experience. Search is foundational to the success of Canva.
Search Pipeline: Part I
Search and Relevance This two-part blog post details the challenges we faced within the Search and Recommendations group in building a scalable search architecture. In part I, we'll discuss the challenges, and in part II, we'll break down the details of our search architecture, and how we take a platform-first approach to enable Canva to build a world-class search experience. Search is foundational to the success of Canva.
Simple, Fast, and Scalable Reverse Image Search Using Perceptual Hashes and DynamoDB
Image Hashing How we built our first iteration of content matching at Canva by Christopher Bong Canva hosts a huge collection of ever-growing, user media that needs to be stored and managed properly. The diversity of this media poses challenges around the moderation and reduction of unnecessary duplicate content. To cater to this, one tool we use is perceptual hashing with an internally built reverse image search system.
Simple, Fast, and Scalable Reverse Image Search Using Perceptual Hashes and DynamoDB
Image Hashing Canva hosts a huge collection of ever-growing, user media that needs to be stored and managed properly. The diversity of this media poses challenges around the moderation and reduction of unnecessary duplicate content. To cater to this, one tool we use is perceptual hashing with an internally built reverse image search system. This blog post describes the design and process we went through to build such a system to the scale of Canva.
We Put Half a Million files in One git Repository, Here’s What We Learned
Monorepo Using a monorepo causes a lot of performance challenges for git. Here’s how we solve them at Canva By Anh Le At Canva, we made the conscious decision to adopt the monorepo pattern with its benefits and drawbacks. Since the first commit in 2012, the repository has rapidly grown alongside the product in both size and traffic. Over the last 10 years, the code base has grown from a few thousand lines to just under 60 million lines of code in 2022.
CSS: Absolutely positioning things relatively
CSS Using CSS grid to render complex webpages responsively By Benjamin Morris As software engineers, we have a plethora of tools available to control the rendering of a webpage (Introduction to CSS layout) and can easily create bespoke user interfaces for different devices (Using media queries). An experienced web developer can manually convert a single design into a set of HTML + CSS that will look great on both your laptop and your mobile phone.
Combining Classic and Modern: A New Approach to Camera Simulation
Computer Vision Applying classic principles from physics and optics to build the best possible camera effects at Canva By Bhautik Joshi We love the look of high-end portraits — that shallow-depth-of-field look where the subject is in-focus but the background is blurred out. But some cameras, especially those on smartphones, don’t quite manage to do this convincingly. Also sometimes, you’d like to be able to adjust the blur after you’ve taken the photo too.
How to design in every language at once
Localization The technology that powers Continuous Localization at Canva By Minh Cung and Simon Hammond At Canva, part of our commitment to inclusivity is building a global design product that’s accessible to everyone in the world. Our vision is to empower the world to design, so one of our crazy big goals is to be available in every language. With over 7,000 languages in the world, that’s a work in progress, but at the time of this post, Canva is available in 104 languages across the globe.
Machine Learning Hyperparameter Optimization with Argo
Machine learning How the hyperparameters of our machine learning models are tuned at Canva by Ryan Lin, Yiran Jing, and Paul Tune Canva uses a variety of machine learning (ML) models, such as recommender systems, information retrieval, attribution models, and natural language processing for various applications. A typical problem is the amount of time and engineering effort in choosing a set of optimal hyperparameters and configurations used to optimize a learning algorithm’s performance.
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