Trillions of requests.
Petabyte scale.
Unprecedented functionality.
Yesterday's hyperbole is today's reality.
Some companies have created systems so large they are difficult to comprehend. Despite fears of AI coming for everyone's jobs, mere mortals are responsible for designing, building, and operating these software behemoths.
For everyone who has sat in awe and thought "how did they do that?" QCon presents Architectures You've Always Wondered About. Continuing a tradition since the first QCon 18 years ago, this flagship track brings together a collection of stories from innovative companies who are pushing the limits with modern software systems.
Be inspired by speakers sharing real-world examples of how they've scaled systems to handle massive amounts of traffic, data, and complexity. Learn from their challenges so you're prepared for your own journey to hyperbolic scale.
From this track
Supporting Diverse ML Systems at Netflix
Netflix uses data science and machine learning across all facets of the company, powering a wide range of business applications.
David Berg
Senior Software Engineer @Netflix
Romain Cledat
Senior Software Engineer @Netflix
Slack's AI-Powered, Hybrid Approach for Large-Scale Migration from Enzyme to React Testing Library
With the Enzyme test framework no longer supporting React 18, migrating to React Testing Library (RTL) became imperative.
Sergii Gorbachov
Senior Software Engineer @Slack
Optimizing Search at Uber Eats
Uber has an in-house search engine called Search In Action (SIA). As the backbone behind the feed and search capabilities of Uber's Delivery business, SIA plays a crucial role in expanding selection seamlessly for customers which is a strategic advantage to the business.
Janani Narayanan
Senior Staff Engineer @Uber
Karthik Ramasamy
Senior Staff Software Engineer @Uber
