Abstract
76%
senior dev or higher
1:11
speaker ratio
60+
practitioners
QCon London 2025 is a three day conference for senior software engineers, architects and team leads. An international program committee of working engineers selects every session. Patterns and practices, not products and pitches.
From the same track
Tuesday 8 April
10:35 Whittle (3rd Fl.) Session AI/ML Deploy MultiModal RAG Systems with vLLM Stephen Batifol Developer Advocate @Zilliz, Founding Member of the MLOps Community Berlin, Previously Machine Learning Engineer @Wolt, and Data Scientist @Brevo While text-based RAG systems have been everywhere in the last year and a half, there is so much more than text data. Images, audio, and documents often need to be processed together to provide meaningful insights, yet most RAG implementations focus solely on text. 11:45 Rutherford (4th Fl.) Unconference Unconference: AI and ML for Software Engineers 13:35 Whittle (3rd Fl.) Session AI/ML AI for Food Image Generation in Production: How & Why Iaroslav Amerkhanov Senior Data Scientist @Delivery Hero, Founder of T4lky, Creator & Host of EPAM Podcast, Speaker In this talk, we will conduct a technical overview of a client-facing Food Image Generation solution developed at Delivery Hero. 14:45 Whittle (3rd Fl.) Session AI/ML Foundation Models for Ranking: Challenges, Successes, and Lessons Learned Moumita Bhattacharya Senior Research Scientist @Netflix, Previously @Etsy Recommender systems are an integral part of most products nowadays and are often a key driver of discovery for users of the product. 15:55 Churchill (Ground Fl.) Session AI/ML Building Embedding Models for Large-Scale Real-World Applications Sahil Dua Senior Software Engineer, Machine Learning @Google, Stanford AI, Co-Author of “The Kubernetes Workshop”, Open-Source Enthusiast Embedding models are at the core of search, recommendation, and retrieval-augmented generation (RAG) systems, transforming data into meaningful representations. 17:05 Whittle (3rd Fl.) Session AI/ML How to Unlock Insights and Enable Discovery Within Petabytes of Autonomous Driving Data Kyra Mozley ML Engineer @Wayve, Previously Security & AI PhD Candidate @Royal Holloway University For autonomous vehicle companies, finding valuable insights within millions of hours of video data is essential yet challenging.