Building an in house image recognition | Lukas Norbutas
🚀 Join One of Europe's Top Software Development Conferences! 🎟 Get your tickets to Build Stuff: 👉 https://www.buildstuff.events/conf 💡 Become a Build Stuff Ambassador to get exclusive perks & rewards: 👉 https://buildstuff.events/pages/ambas... 📩 Lukas Norbutas shares the journey of developing an in-house image recognition system at Vinted to improve the search and recommendation experience for users. By using machine learning models, Vinted can extract meaningful metadata from listings and make the platform more searchable. This talk highlights the challenges, lessons learned, and the technical architecture behind Vinted's image recognition system. 🔍 In This Video, You'll Learn About: ✅ Why Vinted chose to build an in-house image recognition system ✅ How machine learning models improve search relevance for users ✅ Key architecture of the image recognition system ✅ Lessons on the importance of high-quality data for machine learning ✅ How a modular approach to machine learning models accelerates iteration ✅ The role of data pipelines in making the system scalable and efficient
00:00 🚀 Build Stuff Conference – Get Your Tickets!
00:20 Introduction: Why Build an In-House Image Recognition System
03:10 Leveraging Photos to Extract Metadata from Listings
06:15 Technical Architecture: Data Streams and Machine Learning Models
10:00 AB Testing and Model Validation in Production
14:30 Lessons Learned: In-House vs. Outsourcing Image Recognition
18:00 Optimizing Data Pipelines for Fast Iteration and Scalability
19:37 The Importance of a Modular Approach to Machine Learning Models
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